diff --git a/demo/demo_template 1.pptx b/demo/demo_template 1.pptx index b6c2514..dc314a5 100644 Binary files a/demo/demo_template 1.pptx and b/demo/demo_template 1.pptx differ diff --git a/src/README.md b/src/README.md index 2a6bf4f..4db07f8 100644 --- a/src/README.md +++ b/src/README.md @@ -7,62 +7,79 @@ This repository contains all materials for the hackathon focused on building age There are 2 pre-built MCP servers and a Jupyter notebook that can be run to execute LangChain to communicate with these MCP servers. - [Attractions MCP Server](mcp/attractions-mcp/README.md) -- [Weather MCP Server](mcp/weather-mcp/README.md) +- [Weather MCP Server (port 8009)](mcp/weather-mcp/README.md) - [Attractions Jupyter Notebook](agent/README.md) +- [Macros MCP Server (port 8010)](mcp/macros-mcp/README.md) +- [Nutritional Plan MCP Server (port 8011)](mcp/nutritional-plan-mcp/README.md) +- [Trivia MCP Server (port 8012)](mcp/trivia-mcp/README.md) ## Getting Started Follow these steps to set up and run Weather MCP/Attractions MCP service: ### Prerequisites + 1. **Install Python 3.13** + - Download from [python.org](https://www.python.org/downloads/) 2. **Install uv** (Python package manager) + ```bash pip install uv ``` - + **Windows users**: You may need to add the Python Scripts folder to your PATH environment variable: + - If installed globally: `C:\Users\\AppData\Local\Programs\Python\Python313\Scripts\` - If installed with `pip install --user uv`: `C:\Users\\AppData\Roaming\Python\Python313\Scripts\` - Restart your command prompt/PowerShell after updating PATH ### Setup Steps + 1. **Fork and clone the repository** + ```bash git clone cd Hackathon-2025 ``` 2. **Navigate to the weather-mcp or attractions-mcp directory** + ```bash cd src/mcp/weather-mcp ``` + ```bash cd src/mcp/attractions-mcp ``` 3. **Install dependencies** + ```bash uv sync ``` 4. **Run the MCP server** + ```bash uv run mcp dev main.py ``` + The weather MCP service will start and a local UI will launch in the browser (MCP's have to be running to run the jupyter notebook). When using mcp dev you may need to change the support type dropdown to STDIO to use it. > **⚠ Important Note:** If you are running the MCP to be consumed by the agent, do not run it in dev mode. Use the following command instead: +> > ```bash > uv run main.py > ``` ## Agent Notebook + Once your MCP services are running, you can explore the agent implementation in the Jupyter notebook: + - [Attractions Agent Notebook](agent/README.md) ## Creating a New MCP Service @@ -70,16 +87,19 @@ Once your MCP services are running, you can explore the agent implementation in To create your own MCP service, follow these steps: 1. **Navigate to the MCP folder** + ```bash cd src/mcp ``` 2. **Initialize a new MCP project** + ```bash uv init mymcpname-mcp ``` 3. **Add MCP dependencies** + ```bash cd mymcpname-mcp uv add "mcp[cli]" @@ -87,33 +107,34 @@ To create your own MCP service, follow these steps: 4. **Create your MCP service code** Update the `main.py` file with this default MCP code to get you started: - - ```python + + ```python from mcp.server.fastmcp import FastMCP - + # Create an MCP server # you can add the port here so that it doesnt clash with other mcp servers mcp = FastMCP("myname") - + # Add an addition tool @mcp.tool() def add(a: int, b: int) -> int: """Add two numbers""" return a + b - - + + # Add a dynamic greeting resource @mcp.resource("greeting://{name}") def get_greeting(name: str) -> str: """Get a personalized greeting""" return f"Hello, {name}!" - + if __name__ == "__main__": mcp.run("streamable-http") ``` 5. **Run your MCP service** + ```bash uv run mcp dev main.py ``` @@ -121,4 +142,4 @@ To create your own MCP service, follow these steps: 6. **Start building your custom functionality** - Follow the existing patterns in `weather-mcp` or `attractions-mcp` folders - Implement your custom tools and functionality - - for any additional guidance you can check here https://github.com/modelcontextprotocol/python-sdk \ No newline at end of file + - for any additional guidance you can check here https://github.com/modelcontextprotocol/python-sdk diff --git a/src/agent/README.md b/src/agent/README.md index 43e3708..f310011 100644 --- a/src/agent/README.md +++ b/src/agent/README.md @@ -18,6 +18,7 @@ This notebook demonstrates a LangChain agent integrated with MCP (Model Context ## Setup Instructions ### 1. Environment Setup + Create a `.env` file in this directory with your Azure OpenAI credentials and mcp configuration: ```env @@ -27,24 +28,29 @@ AZURE_API_VERSION=2024-12-01-preview DEPLOYMENT_NAME= ATTRACTIONS_MCP_URL=http://127.0.0.1:8008/mcp/ WEATHER_MCP_URL=http://127.0.0.1:8009/mcp/ +MACROS_MCP_URL=http://127.0.0.1:8010/mcp/ +NUTRITION_PLAN_MCP_URL=http://127.0.0.1:8011/mcp/ ``` You can get these credentials from: + - Azure OpenAI service → Azure AI Foundry Portal - Your Azure OpenAI resource in the Azure portal - Overview tab for endpoint, API key (selecting "Azure AI Services" tab within "Libraries") ### 2. Register Jupyter Kernel (Important!) + Make sure you have downloaded the offical [Jupyter VS Code extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter). -**Important:** After opening the notebook file (*.ipynb), make sure to select the correct kernel: +**Important:** After opening the notebook file (\*.ipynb), make sure to select the correct kernel: + 1. Install the offcial jupyter extension 1. After opening the notebook file, click on "Select Kernel" and then "Python Environments...": ![alt text](images/image.png) -2. Select "Create Python Environment" -3. Select "venv` → Choose lastest python" -4. Select the one `src\agent\requirements.txt` dependencies to install. Select ok on the prompt. -4. This ensures you're using the local virtual environment, not a global one +1. Select "Create Python Environment" +1. Select "venv` → Choose lastest python" +1. Select the one `src\agent\requirements.txt` dependencies to install. Select ok on the prompt. +1. This ensures you're using the local virtual environment, not a global one This will create a virtual environment and start installing dependencies. @@ -59,6 +65,7 @@ cd ../../mcp/attractions-mcp # Start the MCP server on HTTP uv run main.py ``` + The server should start on `http://localhost:8008/mcp/` for attractions ```bash @@ -68,6 +75,7 @@ cd ../../mcp/weather-mcp # Start the MCP server on HTTP uv run main.py ``` + The server should start on `http://localhost:8009/mcp/` for weather ### 4. Running the Notebook @@ -75,6 +83,7 @@ The server should start on `http://localhost:8009/mcp/` for weather Open and run the Jupyter notebook: You can verify the correct environment by running Cell 0 - it should show: + ``` python: *CodeDirectory*\Hackathon\Hackathon-2025\.venv\Scripts\python.exe uv: *Python install location*\Python313\Scripts\uv.EXE @@ -91,33 +100,43 @@ You can hit `run all` at the top of the notebook, or you can run the cells 1 by The notebook is structured in sequential cells that should be run in order: ### Step 1: Environment Check + - **Cell 0**: Verifies that the virtual environment is being used correctly -### Step 2: Install Dependencies +### Step 2: Install Dependencies + - **Cell 1**: Installs uv and all required packages from requirements.txt ### Step 3: Import and Setup + - **Cell 2**: Imports all necessary libraries for LangChain and MCP integration ### Step 4-5: MCP Client Configuration + - **Cell 3-4**: Sets up the MCP client to connect to the HTTP server on localhost:8008 ### Step 6: Agent Initialization + - **Cell 5-6**: Creates and initializes the LangChain agent with MCP tools ### Step 7: User Input Handler + - **Cell 7**: Sets up functions to interact with the AI agent ### Step 8: Connection Testing + - **Cell 8**: Tests the MCP server connection and displays available tools ### Step 9: Example Usage + - **Cell 9**: Demonstrates how to use the agent with example queries ### Step 10: Interactive Chat + - **Cell 10**: Interactive chat loop to converse with the agent ### Step 11: Cleanup + - **Cell 11**: Provides cleanup function for proper resource management ## Available MCP Tools @@ -140,11 +159,71 @@ Once the notebook is running, you can ask the assistant questions like: - "What's a random famous attraction I should visit?" - "Show me museums in London" +## FastAPI Server (for Frontend Integration) + +If you want to expose the agent over HTTP so the React app can call it, run the FastAPI server that lives in `src/agent/server`. + +### 1. Configure environment + +Create or update your `.env` (or export variables) with either Azure OpenAI or OpenAI credentials: + +```bash +# Azure (preferred) +AZURE_OPENAI_ENDPOINT= +AZURE_OPENAI_API_KEY= +AZURE_API_VERSION=2024-12-01-preview +DEPLOYMENT_NAME= + +# Optional fallback +OPENAI_API_KEY= +``` + +Ensure the MCP servers you need (attractions, weather, macros, nutritional-plan, trivia) are running; the server will use their HTTP endpoints. + +Default URLs (override in `.env` if needed): + +``` +ATTRACTIONS_MCP_URL=http://127.0.0.1:8008/mcp/ +WEATHER_MCP_URL=http://127.0.0.1:8009/mcp/ +MACROS_MCP_URL=http://127.0.0.1:8010/mcp/ +NUTRITION_PLAN_MCP_URL=http://127.0.0.1:8011/mcp/ +TRIVIA_MCP_URL=http://127.0.0.1:8012/mcp/ +``` + +### 2. Install dependencies & run server + +```bash +cd src/agent +python -m pip install -r requirements.txt +python server/main.py +``` + +The server listens on `http://localhost:8080` by default and exposes: + +- `GET /health` +- `POST /init` → `{ sessionId }` +- `POST /chat` with `{ sessionId?, message }` → `{ sessionId, output }` + +Each session keeps in-memory chat history. Restarting the server clears sessions. +Errors from MCP tools are returned to the caller as friendly messages in the API response frame. + +### 3. Frontend config + +Set the frontend environment variable so it knows where to reach the server: + +``` +src/frontend/.env +VITE_AGENT_URL=http://localhost:8080 +``` + +Then run the frontend (`npm run dev`) and use the chat UI. + ## Troubleshooting ### Common Issues: 1. **MCP Server Connection Failed** + - Ensure the MCP server is running on localhost:8008 - Check that no firewall is blocking the connection @@ -153,9 +232,11 @@ Once the notebook is running, you can ask the assistant questions like: - Check that your Azure OpenAI deployment name matches (default: "gpt-5-chat") ### Debug Mode + The agent runs in verbose mode by default, showing the reasoning process. To see detailed logs, check the console output when running cells. ## Project Structure + ``` src/agent ├── attractions.ipynb # Main notebook file @@ -164,8 +245,9 @@ src/agent ``` ## Next Steps + After setting up the notebook: 1. Run through all cells sequentially -3. Try your own travel-related questions -4. Explore different MCP tools and their capabilities \ No newline at end of file +2. Try your own travel-related questions +3. Explore different MCP tools and their capabilities diff --git a/src/agent/attractions.ipynb b/src/agent/attractions.ipynb index 7d7a7de..6368fc7 100644 --- a/src/agent/attractions.ipynb +++ b/src/agent/attractions.ipynb @@ -2,19 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "4a8c1aa1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "python: c:\\WorkSpace\\Hackathon\\Hackathon-2025\\.venv\\Scripts\\python.exe\n", - "uv: c:\\WorkSpace\\Hackathon\\Hackathon-2025\\.venv\\Scripts\\uv.EXE\n" - ] - } - ], + "outputs": [], "source": [ "# Step to ensure that the venv is being used for the project not local copies, should point at .venv in project.\n", "import sys, shutil\n", @@ -24,27 +15,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "7c3a1676", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: uv in c:\\workspace\\hackathon\\hackathon-2025\\.venv\\lib\\site-packages (0.8.20)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[2mUsing Python 3.13.3 environment at: c:\\WorkSpace\\Hackathon\\Hackathon-2025\\.venv\u001b[0m\n", - "\u001b[2mAudited \u001b[1m13 packages\u001b[0m \u001b[2min 19ms\u001b[0m\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# installs into the current Jupyter kernel environment\n", "%pip install -U uv \n", @@ -54,18 +28,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "f6fdf3d8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Updated imports with official MCP adapter loaded successfully!\n" - ] - } - ], + "outputs": [], "source": [ "# LangChain + MCP Setup for Attractions Booking (HTTP-based for Jupyter)\n", "import os\n", @@ -94,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "f9bc6177", "metadata": {}, "outputs": [ @@ -128,7 +94,23 @@ " \"weather\": {\n", " \"transport\": \"streamable_http\",\n", " \"url\": os.getenv(\"WEATHER_MCP_URL\")\n", - " }\n", + " },\n", + " \"datastore\": {\n", + " \"transport\": \"streamable_http\",\n", + " \"url\": os.getenv(\"DATASTORE_MCP_URL\")\n", + " },\n", + " \"trivia\": {\n", + " \"transport\": \"streamable_http\",\n", + " \"url\": os.getenv(\"TRIVIA_MCP_URL\")\n", + " },\n", + " \"macros\": {\n", + " \"transport\": \"streamable_http\",\n", + " \"url\": os.getenv(\"MACROS_MCP_URL\")\n", + " },\n", + " \"nutrition\": {\n", + " \"transport\": \"streamable_http\",\n", + " \"url\": os.getenv(\"NUTRITIONAL_PLAN_MCP_URL\")\n", + " },\n", " })\n", " \n", " # Get tools from the MCP server\n", @@ -145,18 +127,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "5deceb6f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🛠️ Ready to load MCP tools via official adapter!\n" - ] - } - ], + "outputs": [], "source": [ "# Load MCP Tools using Official Adapter\n", "# The tools will be loaded dynamically when setting up the agent\n", @@ -166,18 +140,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "ab1bb59a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🤖 Agent setup function ready! Run the next cell to initialize.\n" - ] - } - ], + "outputs": [], "source": [ "async def setup_agent():\n", " \"\"\"Setup LangChain agent with MCP tools using official adapter\"\"\"\n", @@ -242,29 +208,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "881d1451", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing agent with MCP tools...\n", - "Loaded 7 MCP tools: ['get_attraction_details', 'search_attractions', 'get_random_attraction', 'book_attraction', 'search_and_format_attractions', 'get_current_weather', 'get_weather_forecast']\n", - "Loaded 7 MCP tools: ['get_attraction_details', 'search_attractions', 'get_random_attraction', 'book_attraction', 'search_and_format_attractions', 'get_current_weather', 'get_weather_forecast']\n", - "LangChain agent with MCP tools ready!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\JayGregory\\AppData\\Local\\Temp\\ipykernel_51000\\3851623357.py:50: LangChainDeprecationWarning: Please see the migration guide at: https://python.langchain.com/docs/versions/migrating_memory/\n", - " memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n" - ] - } - ], + "outputs": [], "source": [ "# Initialize the agent with MCP tools\n", "async def initialize_agent():\n", @@ -283,18 +230,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "ba56fc7e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💬 User input handler ready!\n" - ] - } - ], + "outputs": [], "source": [ "# User Input Handler + logged agent steps\n", "async def process_user_input(user_input: str) -> str:\n", @@ -347,87 +286,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "4d4447a3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded 7 MCP tools: ['get_attraction_details', 'search_attractions', 'get_random_attraction', 'book_attraction', 'search_and_format_attractions', 'get_current_weather', 'get_weather_forecast']\n", - "MCP HTTP server connected successfully!\n", - "Available tools: ['get_attraction_details', 'search_attractions', 'get_random_attraction', 'book_attraction', 'search_and_format_attractions', 'get_current_weather', 'get_weather_forecast']\n", - " - get_attraction_details: Get detailed information about a specific tourist attraction\n", - "\n", - "Args:\n", - " attraction_id: Unique ID of the attraction\n", - " \n", - "Returns:\n", - " AttractionDetails object as dictionary with attraction info, facilities, and visiting tips\n", - "\n", - " - search_attractions: Search for tourist attractions with optional filters\n", - "\n", - "Args:\n", - " location: Location to search in (e.g., \"Paris\", \"India\", \"Italy\")\n", - " category: Category filter - \"historical\", \"natural\", \"cultural\", \"religious\", \"modern\", \"museums\", \"parks\", \"beaches\", \"mountains\", \"architecture\", \"entertainment\", \"adventure\"\n", - " limit: Maximum number of results (1-100, default: 20)\n", - " \n", - "Returns:\n", - " AttractionsList object as dictionary with matching attractions\n", - "\n", - " - get_random_attraction: Get a random tourist attraction for inspiration\n", - "\n", - "Args:\n", - " region: Region type - \"famous\" for world famous attractions, \"india\" for Indian attractions\n", - " \n", - "Returns:\n", - " Attraction object as dictionary with random attraction details\n", - "\n", - " - book_attraction: Book a visit to a tourist attraction\n", - "\n", - "Args:\n", - " attraction_id: ID of the attraction to book\n", - " visitor_name: Name of the primary visitor\n", - " email: Email address for booking confirmation\n", - " visit_date: Visit date in YYYY-MM-DD format\n", - " num_visitors: Number of visitors (1-50, default: 1)\n", - " phone: Optional phone number\n", - " special_requirements: Optional special requirements or requests\n", - " \n", - "Returns:\n", - " BookingResponse object as dictionary with booking confirmation details\n", - "\n", - " - search_and_format_attractions: Search for attractions and return formatted results for easy reading\n", - "\n", - "Args:\n", - " location: Location to search in (e.g., \"Paris\", \"India\", \"Italy\") \n", - " category: Category filter (e.g., \"historical\", \"natural\", \"cultural\")\n", - " limit: Maximum number of results (1-20, default: 10)\n", - " \n", - "Returns:\n", - " Formatted string with attraction search results\n", - "\n", - " - get_current_weather: Get current weather information for a specific location\n", - "\n", - "Args:\n", - " location: City name or place name (e.g., \"London\", \"New York\", \"Tokyo\")\n", - " \n", - "Returns:\n", - " CurrentWeather object as dictionary or error dict\n", - "\n", - " - get_weather_forecast: Get weather forecast for a specific location\n", - "\n", - "Args:\n", - " location: City name or place name (e.g., \"London\", \"New York\", \"Tokyo\")\n", - " days: Number of days for forecast (1-16, default is 7)\n", - " \n", - "Returns:\n", - " WeatherForecast object as dictionary or error dict\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Test MCP server connectivity and tools\n", "async def test_mcp_connection():\n", @@ -447,115 +309,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "57dcf6ff", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🧳 User: What are some popular attractions in Paris? and what is the weather?\n", - "🤖 Assistant:\n", - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m\n", - "Invoking: `search_and_format_attractions` with `{'location': 'Paris', 'limit': 10}`\n", - "\n", - "\n", - "\u001b[0m\u001b[32;1m\u001b[1;3m\n", - "Invoking: `get_current_weather` with `{'location': 'Paris'}`\n", - "\n", - "\n", - "\u001b[0m\u001b[36;1m\u001b[1;3m🎯 Found 2 attractions in Paris:\n", - "\n", - "1. 🏛️ **Eiffel Tower**\n", - " 📍 Paris, France\n", - " 🏷️ Architecture\n", - " ⭐ 4.6/5.0\n", - " 💰 €29.40 - €73.30\n", - "\n", - "2. 🏛️ **Louvre Museum**\n", - " 📍 Paris, France\n", - " 🏷️ Museums\n", - " ⭐ 4.4/5.0\n", - " 💰 €17\n", - "\n", - "\u001b[0m\u001b[33;1m\u001b[1;3m{\n", - " \"location\": \"Paris, Île-de-France, France\",\n", - " \"coordinates\": {\n", - " \"lat\": 48.85341,\n", - " \"lon\": 2.3488\n", - " },\n", - " \"temperature\": {\n", - " \"current\": 12.0,\n", - " \"feels_like\": 10.2,\n", - " \"min\": null,\n", - " \"max\": null,\n", - " \"unit\": \"°C\"\n", - " },\n", - " \"weather\": {\n", - " \"description\": \"Slight rain\",\n", - " \"code\": 61\n", - " },\n", - " \"wind\": {\n", - " \"speed\": 16.1,\n", - " \"direction\": 20,\n", - " \"unit\": \"km/h\",\n", - " \"max_gusts\": null\n", - " },\n", - " \"humidity\": 64,\n", - " \"pressure\": 1018.1,\n", - " \"precipitation\": 0.6,\n", - " \"timezone\": \"Europe/Paris\",\n", - " \"timestamp\": \"2025-09-23T11:45\"\n", - "}\u001b[0m\u001b[32;1m\u001b[1;3mHere are a few quick highlights and the current weather for Paris.\n", - "\n", - "Popular attractions (quick overview)\n", - "- Eiffel Tower — Architecture — Rating ~4.6/5 — Typical ticket range €29–€73\n", - " - Tip: Book a timed ticket in advance to avoid long queues; allow 1–2 hours; great at sunset.\n", - "- Louvre Museum — Museums — Rating ~4.4/5 — Typical ticket ~€17\n", - " - Tip: Closed on Tuesdays; get a timed-entry ticket and plan 2–4 hours (Mona Lisa area is busiest).\n", - "\n", - "Current weather in Paris (Île‑de‑France) — as of 2025-09-23 11:45 (local)\n", - "- Temperature: 12.0°C (feels like 10.2°C)\n", - "- Conditions: Slight rain\n", - "- Precipitation: ~0.6 mm\n", - "- Wind: ~16 km/h from ~20° (NNE)\n", - "- Humidity: 64% · Pressure: ~1018 hPa\n", - "\n", - "Practical suggestions\n", - "- Pack a compact umbrella or a waterproof jacket and wear layers — cool and light rain.\n", - "- For outdoor sights (Eiffel Tower, Seine strolls) prioritize mornings or late afternoons to avoid peak crowds.\n", - "- Want me to: find more attractions by neighborhood or category, get detailed info (hours, accessibility), or book tickets for the Eiffel Tower or Louvre? If you want booking, tell me the visit date, number of visitors, and the name/email to use.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "Here are a few quick highlights and the current weather for Paris.\n", - "\n", - "Popular attractions (quick overview)\n", - "- Eiffel Tower — Architecture — Rating ~4.6/5 — Typical ticket range €29–€73\n", - " - Tip: Book a timed ticket in advance to avoid long queues; allow 1–2 hours; great at sunset.\n", - "- Louvre Museum — Museums — Rating ~4.4/5 — Typical ticket ~€17\n", - " - Tip: Closed on Tuesdays; get a timed-entry ticket and plan 2–4 hours (Mona Lisa area is busiest).\n", - "\n", - "Current weather in Paris (Île‑de‑France) — as of 2025-09-23 11:45 (local)\n", - "- Temperature: 12.0°C (feels like 10.2°C)\n", - "- Conditions: Slight rain\n", - "- Precipitation: ~0.6 mm\n", - "- Wind: ~16 km/h from ~20° (NNE)\n", - "- Humidity: 64% · Pressure: ~1018 hPa\n", - "\n", - "Practical suggestions\n", - "- Pack a compact umbrella or a waterproof jacket and wear layers — cool and light rain.\n", - "- For outdoor sights (Eiffel Tower, Seine strolls) prioritize mornings or late afternoons to avoid peak crowds.\n", - "- Want me to: find more attractions by neighborhood or category, get detailed info (hours, accessibility), or book tickets for the Eiffel Tower or Louvre? If you want booking, tell me the visit date, number of visitors, and the name/email to use.\n", - "\n", - "==================================================\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# 🚀 EXAMPLE USAGE - Run this cell after setting up your API key!\n", "\n", @@ -570,295 +327,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 47, "id": "bd0841c0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type 'exit' to quit. Press Enter on an empty line to skip.\n", - "🧳 User: give me a random attraction\n", - "🤖 Assistant:\n", - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m\n", - "Invoking: `get_random_attraction` with `{'region': 'famous'}`\n", - "\n", - "\n", - "\u001b[0m\u001b[38;5;200m\u001b[1;3m{\n", - " \"id\": 10,\n", - " \"name\": \"Petra\",\n", - " \"description\": \"Archaeological city famous for rock-cut architecture and water conduit system\",\n", - " \"category\": \"historical\",\n", - " \"location\": {\n", - " \"city\": \"Ma'an\",\n", - " \"country\": \"Jordan\",\n", - " \"region\": \"Ma'an\",\n", - " \"coordinates\": null\n", - " },\n", - " \"rating\": 4.7,\n", - " \"image_url\": \"https://example.com/petra.jpg\",\n", - " \"website\": \"https://www.visitpetra.jo\",\n", - " \"opening_hours\": \"6:00 AM - 6:00 PM\",\n", - " \"entry_fee\": \"70 JOD (1 day), 55 JOD (2 days)\"\n", - "}\u001b[0m\u001b[32;1m\u001b[1;3mGreat choice — here’s a random pick:\n", - "\n", - "Petra (Jordan)\n", - "- What it is: Ancient archaeological city famous for its rock-cut architecture (like the Treasury) and ancient water conduit system.\n", - "- Location: Ma'an Governorate, Jordan.\n", - "- Category: Historical.\n", - "- Rating: 4.7.\n", - "- Opening hours: 6:00 AM – 6:00 PM.\n", - "- Entry fee: 70 JOD (1 day), 55 JOD (2 days).\n", - "- Website: https://www.visitpetra.jo\n", - "- Recommended visit length: 4–6 hours (longer if you plan hikes to the Monastery or Al-Khubtha).\n", - "- Best time to visit: Spring or autumn for milder weather; arrive early to avoid crowds and heat.\n", - "- Practical tips: wear sturdy walking shoes, bring plenty of water and sun protection, consider a guided tour to learn the history, and note some trails can be steep. Petra by Night is a special evening experience on some nights — check schedules in advance.\n", - "\n", - "Would you like more details (hours, accessibility, guided tours), help booking a ticket or tour, or another random attraction?\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "Great choice — here’s a random pick:\n", - "\n", - "Petra (Jordan)\n", - "- What it is: Ancient archaeological city famous for its rock-cut architecture (like the Treasury) and ancient water conduit system.\n", - "- Location: Ma'an Governorate, Jordan.\n", - "- Category: Historical.\n", - "- Rating: 4.7.\n", - "- Opening hours: 6:00 AM – 6:00 PM.\n", - "- Entry fee: 70 JOD (1 day), 55 JOD (2 days).\n", - "- Website: https://www.visitpetra.jo\n", - "- Recommended visit length: 4–6 hours (longer if you plan hikes to the Monastery or Al-Khubtha).\n", - "- Best time to visit: Spring or autumn for milder weather; arrive early to avoid crowds and heat.\n", - "- Practical tips: wear sturdy walking shoes, bring plenty of water and sun protection, consider a guided tour to learn the history, and note some trails can be steep. Petra by Night is a special evening experience on some nights — check schedules in advance.\n", - "\n", - "Would you like more details (hours, accessibility, guided tours), help booking a ticket or tour, or another random attraction?\n", - "🧳 User: what about attractions in paris\n", - "🤖 Assistant:\n", - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m\n", - "Invoking: `search_and_format_attractions` with `{'location': 'Paris', 'category': None, 'limit': 10}`\n", - "\n", - "\n", - "\u001b[0m\u001b[36;1m\u001b[1;3m🎯 Found 2 attractions in Paris:\n", - "\n", - "1. 🏛️ **Eiffel Tower**\n", - " 📍 Paris, France\n", - " 🏷️ Architecture\n", - " ⭐ 4.6/5.0\n", - " 💰 €29.40 - €73.30\n", - "\n", - "2. 🏛️ **Louvre Museum**\n", - " 📍 Paris, France\n", - " 🏷️ Museums\n", - " ⭐ 4.4/5.0\n", - " 💰 €17\n", - "\n", - "\u001b[0m\u001b[32;1m\u001b[1;3m\n", - "Invoking: `search_attractions` with `{'location': 'Paris', 'category': None, 'limit': 10}`\n", - "\n", - "\n", - "\u001b[0m\u001b[33;1m\u001b[1;3m{\n", - " \"category\": \"All Categories\",\n", - " \"location\": \"Paris\",\n", - " \"total_count\": 2,\n", - " \"attractions\": [\n", - " {\n", - " \"id\": 1,\n", - " \"name\": \"Eiffel Tower\",\n", - " \"description\": \"Iconic iron lattice tower located on the Champ de Mars in Paris, France\",\n", - " \"category\": \"architecture\",\n", - " \"location\": {\n", - " \"city\": \"Paris\",\n", - " \"country\": \"France\",\n", - " \"region\": \"Île-de-France\",\n", - " \"coordinates\": null\n", - " },\n", - " \"rating\": 4.6,\n", - " \"image_url\": \"https://example.com/eiffel-tower.jpg\",\n", - " \"website\": \"https://www.toureiffel.paris\",\n", - " \"opening_hours\": \"9:30 AM - 11:45 PM\",\n", - " \"entry_fee\": \"€29.40 - €73.30\"\n", - " },\n", - " {\n", - " \"id\": 5,\n", - " \"name\": \"Louvre Museum\",\n", - " \"description\": \"World's largest art museum and historic monument in Paris\",\n", - " \"category\": \"museums\",\n", - " \"location\": {\n", - " \"city\": \"Paris\",\n", - " \"country\": \"France\",\n", - " \"region\": \"Île-de-France\",\n", - " \"coordinates\": null\n", - " },\n", - " \"rating\": 4.4,\n", - " \"image_url\": \"https://example.com/louvre.jpg\",\n", - " \"website\": \"https://www.louvre.fr\",\n", - " \"opening_hours\": \"9:00 AM - 6:00 PM\",\n", - " \"entry_fee\": \"€17\"\n", - " }\n", - " ]\n", - "}\u001b[0m\u001b[32;1m\u001b[1;3mGreat — here are popular Paris attractions with quick notes and tips. Tell me which ones you want more details or to book.\n", - "\n", - "- Eiffel Tower (architecture) — Iconic iron tower on the Champ de Mars. Opening hours: ~9:30 AM–11:45 PM. Tickets range ~€29–€73 depending on access/age. Tip: book a timed-entry or summit ticket in advance; sunset or night views are spectacular.\n", - "\n", - "- Louvre Museum (museums) — World’s largest art museum (home of the Mona Lisa). Opening hours: ~9:00 AM–6:00 PM; entry ~€17. Tip: closed some weekdays (check before you go), use a timed-entry ticket and allow 2–4 hours (or more).\n", - "\n", - "Other top picks to consider:\n", - "- Notre‑Dame Cathedral (historic landmark) — note: restoration work continues after the 2019 fire; access may be limited.\n", - "- Musée d’Orsay — Impressionist and post‑Impressionist masterpieces in a former railway station.\n", - "- Sacré‑Cœur and Montmartre — hilltop basilica with great city views and a bohemian neighborhood to explore.\n", - "- Arc de Triomphe & Champs‑Élysées — climb the Arc for views; stroll the avenue for shopping and cafés.\n", - "- Sainte‑Chapelle — stunning medieval stained-glass chapel on Île de la Cité.\n", - "- Seine river cruise (Bateaux Mouches or similar) — relaxing way to see many riverside monuments.\n", - "- Luxembourg Gardens — peaceful park for a stroll or picnic.\n", - "- Palace of Versailles (day trip) — lavish palace and gardens just outside Paris; allow a full day.\n", - "\n", - "Would you like:\n", - "- Details for any specific attraction (hours, accessibility, guided tours)?\n", - "- Help booking tickets (tell me date, number of visitors, name/email)?\n", - "- Current weather for Paris right now or a forecast for your travel dates?\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "Great — here are popular Paris attractions with quick notes and tips. Tell me which ones you want more details or to book.\n", - "\n", - "- Eiffel Tower (architecture) — Iconic iron tower on the Champ de Mars. Opening hours: ~9:30 AM–11:45 PM. Tickets range ~€29–€73 depending on access/age. Tip: book a timed-entry or summit ticket in advance; sunset or night views are spectacular.\n", - "\n", - "- Louvre Museum (museums) — World’s largest art museum (home of the Mona Lisa). Opening hours: ~9:00 AM–6:00 PM; entry ~€17. Tip: closed some weekdays (check before you go), use a timed-entry ticket and allow 2–4 hours (or more).\n", - "\n", - "Other top picks to consider:\n", - "- Notre‑Dame Cathedral (historic landmark) — note: restoration work continues after the 2019 fire; access may be limited.\n", - "- Musée d’Orsay — Impressionist and post‑Impressionist masterpieces in a former railway station.\n", - "- Sacré‑Cœur and Montmartre — hilltop basilica with great city views and a bohemian neighborhood to explore.\n", - "- Arc de Triomphe & Champs‑Élysées — climb the Arc for views; stroll the avenue for shopping and cafés.\n", - "- Sainte‑Chapelle — stunning medieval stained-glass chapel on Île de la Cité.\n", - "- Seine river cruise (Bateaux Mouches or similar) — relaxing way to see many riverside monuments.\n", - "- Luxembourg Gardens — peaceful park for a stroll or picnic.\n", - "- Palace of Versailles (day trip) — lavish palace and gardens just outside Paris; allow a full day.\n", - "\n", - "Would you like:\n", - "- Details for any specific attraction (hours, accessibility, guided tours)?\n", - "- Help booking tickets (tell me date, number of visitors, name/email)?\n", - "- Current weather for Paris right now or a forecast for your travel dates?\n", - "🧳 User: book eiffel tower\n", - "🤖 Assistant:\n", - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m\n", - "Invoking: `search_attractions` with `{'location': 'Paris', 'category': 'architecture', 'limit': 20}`\n", - "\n", - "\n", - "\u001b[0m\u001b[33;1m\u001b[1;3m{\n", - " \"category\": \"Architecture\",\n", - " \"location\": \"Paris\",\n", - " \"total_count\": 1,\n", - " \"attractions\": [\n", - " {\n", - " \"id\": 1,\n", - " \"name\": \"Eiffel Tower\",\n", - " \"description\": \"Iconic iron lattice tower located on the Champ de Mars in Paris, France\",\n", - " \"category\": \"architecture\",\n", - " \"location\": {\n", - " \"city\": \"Paris\",\n", - " \"country\": \"France\",\n", - " \"region\": \"Île-de-France\",\n", - " \"coordinates\": null\n", - " },\n", - " \"rating\": 4.6,\n", - " \"image_url\": \"https://example.com/eiffel-tower.jpg\",\n", - " \"website\": \"https://www.toureiffel.paris\",\n", - " \"opening_hours\": \"9:30 AM - 11:45 PM\",\n", - " \"entry_fee\": \"€29.40 - €73.30\"\n", - " }\n", - " ]\n", - "}\u001b[0m\u001b[32;1m\u001b[1;3mI can book the Eiffel Tower for you — I just need a few details first:\n", - "\n", - "1) Visit date (YYYY-MM-DD) \n", - "2) Number of visitors (1–50) \n", - "3) Primary visitor name (for the booking) \n", - "4) Email for confirmation \n", - "5) Phone number (optional) \n", - "6) Any special requests or ticket preference (e.g., summit access vs 2nd floor only, stairs vs elevator, preferred time window like morning/afternoon/evening)\n", - "\n", - "Tell me those and I’ll proceed to check availability and make the booking. Would you also like me to include current weather for Paris on your visit date?\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "I can book the Eiffel Tower for you — I just need a few details first:\n", - "\n", - "1) Visit date (YYYY-MM-DD) \n", - "2) Number of visitors (1–50) \n", - "3) Primary visitor name (for the booking) \n", - "4) Email for confirmation \n", - "5) Phone number (optional) \n", - "6) Any special requests or ticket preference (e.g., summit access vs 2nd floor only, stairs vs elevator, preferred time window like morning/afternoon/evening)\n", - "\n", - "Tell me those and I’ll proceed to check availability and make the booking. Would you also like me to include current weather for Paris on your visit date?\n", - "🧳 User: 1992-04-2992\n", - "🤖 Assistant:\n", - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3mThe date you sent looks invalid: \"1992-04-2992\" — it has extra characters and/or is in the past. I can’t book a past date. Could you please confirm the booking details using this form?\n", - "\n", - "1) Visit date (YYYY-MM-DD) — must be a future date \n", - "2) Number of visitors (1–50) \n", - "3) Primary visitor name \n", - "4) Email for confirmation \n", - "5) Phone (optional) \n", - "6) Ticket preference / special requests (e.g., summit access vs 2nd floor only; stairs vs elevator; preferred time of day)\n", - "\n", - "If you want, type everything in one line (for example):\n", - "2025-10-15, 2, Alex Martin, alex@example.com, +33-6-12-34-56-78, summit ticket, evening\n", - "\n", - "Tell me the corrected details and I’ll check availability and proceed. Would you also like the weather forecast for your visit date?\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "The date you sent looks invalid: \"1992-04-2992\" — it has extra characters and/or is in the past. I can’t book a past date. Could you please confirm the booking details using this form?\n", - "\n", - "1) Visit date (YYYY-MM-DD) — must be a future date \n", - "2) Number of visitors (1–50) \n", - "3) Primary visitor name \n", - "4) Email for confirmation \n", - "5) Phone (optional) \n", - "6) Ticket preference / special requests (e.g., summit access vs 2nd floor only; stairs vs elevator; preferred time of day)\n", - "\n", - "If you want, type everything in one line (for example):\n", - "2025-10-15, 2, Alex Martin, alex@example.com, +33-6-12-34-56-78, summit ticket, evening\n", - "\n", - "Tell me the corrected details and I’ll check availability and proceed. Would you also like the weather forecast for your visit date?\n", - "🧳 User: escape\n", - "🤖 Assistant:\n", - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3mDo you mean one of the following?\n", - "\n", - "1) Cancel/stop the Eiffel Tower booking process (I’ll drop it and we won’t book). \n", - "2) “Escape rooms” in Paris (I can find and book an escape-room experience). \n", - "3) A short getaway/day‑trip from Paris (ideas like Versailles, Giverny, Fontainebleau). \n", - "4) Something else — please tell me what you mean.\n", - "\n", - "Reply with the option number (or explain) and I’ll proceed.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n", - "Do you mean one of the following?\n", - "\n", - "1) Cancel/stop the Eiffel Tower booking process (I’ll drop it and we won’t book). \n", - "2) “Escape rooms” in Paris (I can find and book an escape-room experience). \n", - "3) A short getaway/day‑trip from Paris (ideas like Versailles, Giverny, Fontainebleau). \n", - "4) Something else — please tell me what you mean.\n", - "\n", - "Reply with the option number (or explain) and I’ll proceed.\n", - "Goodbye!\n" - ] - } - ], + "outputs": [], "source": [ "# Interactive chat loop — keep asking questions until you exit\n", "\n", @@ -890,18 +362,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 48, "id": "968731b7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🧹 Cleanup function ready!\n" - ] - } - ], + "outputs": [], "source": [ "# Cleanup function for HTTP MCP client\n", "async def cleanup_mcp():\n", @@ -935,7 +399,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.3" + "version": "3.13.7" } }, "nbformat": 4, diff --git a/src/agent/env.example b/src/agent/env.example new file mode 100644 index 0000000..2213ca1 --- /dev/null +++ b/src/agent/env.example @@ -0,0 +1,11 @@ +AZURE_OPENAI_ENDPOINT=PLEASE-SET-ME +AZURE_OPENAI_API_KEY=PLEASE-SET-ME +AZURE_API_VERSION=PLEASE-SET-ME +DEPLOYMENT_NAME=PLEASE-SET-ME +ATTRACTIONS_MCP_URL=http://127.0.0.1:8008/mcp/ +WEATHER_MCP_URL=http://127.0.0.1:8009/mcp/ +MACROS_MCP_URL=http://127.0.0.1:8010/mcp/ +NUTRITION_PLAN_MCP_URL=http://127.0.0.1:8011/mcp/ +RECIPEMAKER_MCP_URL=http://127.0.0.1:8123/mcp/ +TRIVIA_MCP_URL=http://127.0.0.1:8012/mcp/ +RESTAURANTS_MCP_URL=http://127.0.0.1:8014/mcp/ \ No newline at end of file diff --git a/src/agent/requirements.txt b/src/agent/requirements.txt index 830067d..fba5be8 100644 --- a/src/agent/requirements.txt +++ b/src/agent/requirements.txt @@ -17,4 +17,6 @@ requests>=2.31.0 # Additional utilities python-dotenv>=1.0.0 httpx>=0.26.0 -dotenv>=0.9.9 \ No newline at end of file +dotenv>=0.9.9 +fastapi>=0.115.0 +uvicorn[standard]>=0.30.0 \ No newline at end of file diff --git a/src/agent/server/agent_factory.py b/src/agent/server/agent_factory.py new file mode 100644 index 0000000..1c9fdb4 --- /dev/null +++ b/src/agent/server/agent_factory.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +from typing import Dict, List + +from langchain.agents import AgentExecutor, create_tool_calling_agent +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.runnables.history import RunnableWithMessageHistory +from langchain_community.chat_message_histories import ChatMessageHistory +from langchain_core.messages import SystemMessage +from mcp_loader import load_mcp_tools_sync + + +def _build_prompt(system_prompt: str, tool_descriptions: List[str]) -> ChatPromptTemplate: + tool_section = "\n".join(f"- {line}" for line in tool_descriptions if line) + instructions = system_prompt + if tool_section: + instructions += "\n\nYou can call the following tools when needed:\n" + tool_section + aim = ( + "Use tools to fetch macro estimates, or nutrition plans " + "whenever it helps answer the user." + ) + instructions += f"\n\n{aim}" + return ChatPromptTemplate.from_messages( + [ + ("system", instructions), + MessagesPlaceholder(variable_name="chat_history"), + ("human", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ] + ) + + +def create_agent_runner(llm, system_prompt: str): + tools, tool_descriptions = load_mcp_tools_sync() + prompt = _build_prompt(system_prompt, tool_descriptions) + agent = create_tool_calling_agent(llm, tools, prompt) + executor = AgentExecutor(agent=agent, tools=tools, verbose=True) + + history_store: Dict[str, ChatMessageHistory] = {} + + def get_history(config: Dict): + if isinstance(config, str): + session_id = config + else: + session_id = config.get("configurable", {}).get("session_id", "default") + history = history_store.get(session_id) + if history is None: + history = ChatMessageHistory() + history.add_message(SystemMessage(content=prompt.messages[0].prompt.template)) + history_store[session_id] = history + return history + + runnable = RunnableWithMessageHistory( + executor, + get_history, + input_messages_key="input", + history_messages_key="chat_history", + ) + + return runnable + + diff --git a/src/agent/server/main.py b/src/agent/server/main.py new file mode 100644 index 0000000..d9337ed --- /dev/null +++ b/src/agent/server/main.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import os +import uuid +from typing import Dict, List, Optional + +from dotenv import load_dotenv +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel + +# LangChain / OpenAI +from langchain_openai import ChatOpenAI, AzureChatOpenAI +from langchain_core.tools import ToolException + +from agent_factory import create_agent_runner + +load_dotenv() + +app = FastAPI(title="Agent Server", version="0.1.0") + +# Allow local frontend +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +class InitResponse(BaseModel): + sessionId: str + + +class ChatRequest(BaseModel): + sessionId: Optional[str] = None + message: str + + +class ChatResponse(BaseModel): + sessionId: str + output: str + + +def _get_llm(): + # Prefer Azure if configured + if ( + os.getenv("AZURE_OPENAI_ENDPOINT") + and os.getenv("AZURE_OPENAI_API_KEY") + and os.getenv("DEPLOYMENT_NAME") + ): + return AzureChatOpenAI( + azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), + api_key=os.getenv("AZURE_OPENAI_API_KEY"), + api_version=os.getenv("AZURE_API_VERSION", "2024-12-01-preview"), + deployment_name=os.getenv("DEPLOYMENT_NAME"), + temperature=1, + ) + # Fallback to OpenAI + return ChatOpenAI(model="gpt-4o-mini", temperature=0.3) + + +SYSTEM_PROMPT = ( + "You are an assistant helping with macros, nutrition planning, restaurants (and their menus) and trivia. " + "Ask clarifying questions when needed and provide concise, actionable answers." +) + + +AGENT_RUNNER = create_agent_runner(_get_llm(), SYSTEM_PROMPT) + + +def _ensure_session_id(session_id: Optional[str]) -> str: + return session_id or str(uuid.uuid4()) + + +@app.get("/health") +def health(): + return {"status": "ok"} + + +@app.post("/init", response_model=InitResponse) +def init_session(): + sid = _ensure_session_id(None) + return InitResponse(sessionId=sid) + + +@app.post("/chat", response_model=ChatResponse) +async def chat(req: ChatRequest): + session_id = _ensure_session_id(req.sessionId) + try: + response = await AGENT_RUNNER.ainvoke( + {"input": req.message}, + config={"configurable": {"session_id": session_id}}, + ) + output = response.get("output") if isinstance(response, dict) else str(response) + return ChatResponse(sessionId=session_id, output=output) + except ToolException as tool_err: + message = ( + "Sorry, one of the MCP tools failed: " + f"{tool_err}. Please supply the required details or try again." + ) + return ChatResponse(sessionId=session_id, output=message) + except Exception as exc: + raise HTTPException(status_code=500, detail=str(exc)) from exc + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8080) diff --git a/src/agent/server/mcp_loader.py b/src/agent/server/mcp_loader.py new file mode 100644 index 0000000..0060e8e --- /dev/null +++ b/src/agent/server/mcp_loader.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +import asyncio +import os +from typing import Dict, List, Tuple + +from langchain_mcp_adapters.client import MultiServerMCPClient +from langchain_mcp_adapters.sessions import StreamableHttpConnection + +MCP_CONFIG: Tuple[Tuple[str, str, str], ...] = ( + ("attractions", "ATTRACTIONS_MCP_URL", "http://127.0.0.1:8008/mcp/"), + ("weather", "WEATHER_MCP_URL", "http://127.0.0.1:8009/mcp/"), + ("macros", "MACROS_MCP_URL", "http://127.0.0.1:8010/mcp/"), + ("nutrition_plan", "NUTRITION_PLAN_MCP_URL", "http://127.0.0.1:8011/mcp/"), + ("trivia", "TRIVIA_MCP_URL", "http://127.0.0.1:8012/mcp/"), + ("restaurants", "RESTAURANTS_MCP_URL", "http://127.0.0.1:8014/mcp/"), +) + + +def _build_connections() -> Tuple[Dict[str, StreamableHttpConnection], List[str]]: + connections: Dict[str, StreamableHttpConnection] = {} + notes: List[str] = [] + for name, env_key, default in MCP_CONFIG: + url = os.getenv(env_key, default) + if not url: + continue + connections[name] = StreamableHttpConnection( + transport="streamable_http", url=url + ) + notes.append(f"[{name}] {url}") + return connections, notes + + +async def _load_tools_async( + connections: Dict[str, StreamableHttpConnection], notes: List[str] +): + client = MultiServerMCPClient(connections) + collected = [] + descriptions = list(notes) + + for server_name in connections.keys(): + try: + tools = await client.get_tools(server_name=server_name) + collected.extend(tools) + for tool in tools: + desc = tool.description or "" + label = f"{server_name}:{tool.name}" + descriptions.append(f"{label} - {desc}" if desc else label) + except Exception as exc: # noqa: BLE001 + descriptions.append(f"{server_name} (failed: {exc})") + + return collected, descriptions + + +def load_mcp_tools_sync() -> Tuple[List, List[str]]: + connections, notes = _build_connections() + if not connections: + return [], notes + + try: + return asyncio.run(_load_tools_async(connections, notes)) + except RuntimeError: + # Already inside an event loop (e.g. notebook); create a new loop + loop = asyncio.new_event_loop() + try: + asyncio.set_event_loop(loop) + return loop.run_until_complete(_load_tools_async(connections, notes)) + finally: + loop.close() + asyncio.set_event_loop(None) diff --git a/src/frontend/.gitignore b/src/frontend/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/src/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/src/frontend/README.md b/src/frontend/README.md new file mode 100644 index 0000000..28fa5d2 --- /dev/null +++ b/src/frontend/README.md @@ -0,0 +1,67 @@ +# Frontend (Vite + React + MUI) + +A minimal React frontend scaffolded with Vite, TypeScript, and MUI. + +## Prerequisites + +- Node.js 18+ +- npm (or pnpm/yarn) + +## Install + +```bash +cd src/frontend +npm install +``` + +## Develop + +```bash +npm run dev +``` + +Then open the URL shown in the terminal (typically http://localhost:5173). + +## Lint + +```bash +npm run lint +``` + +## Build + +```bash +npm run build +``` + +Preview the production build locally: + +```bash +npm run preview +``` + +## Tech stack + +- React 19 + TypeScript +- Vite (rolldown-vite) +- MUI 7 for UI components +- ESLint + Prettier + +## Project structure + +``` +src/frontend +├─ src/ +│ ├─ App.tsx # Root component (MUI Stack example) +│ ├─ main.tsx # React root + CssBaseline +│ └─ index.css # Global styles +├─ package.json # Scripts and dependencies +├─ tsconfig*.json # TypeScript configs +└─ eslint.config.js # ESLint setup +``` + +## Customization + +- Update `App.tsx` to add pages or components. +- Add a MUI theme provider if you want custom theming. +- Integrate with MCP backends by adding fetch calls or a small client. diff --git a/src/frontend/eslint.config.js b/src/frontend/eslint.config.js new file mode 100644 index 0000000..6722597 --- /dev/null +++ b/src/frontend/eslint.config.js @@ -0,0 +1,25 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' +import prettier from 'eslint-config-prettier' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs['recommended-latest'], + reactRefresh.configs.vite, + prettier, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/src/frontend/index.html b/src/frontend/index.html new file mode 100644 index 0000000..072a57e --- /dev/null +++ b/src/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + frontend + + +
+ + + diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json new file mode 100644 index 0000000..172dbdd --- /dev/null +++ b/src/frontend/package-lock.json @@ -0,0 +1,3915 @@ +{ + "name": "frontend", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.0.0", + "dependencies": { + "@emotion/react": "^11.14.0", + "@emotion/styled": "^11.14.1", + "@mui/material": "^7.3.2", + "react": "^19.1.1", + "react-dom": "^19.1.1", + "react-icons": "^5.5.0" + }, + "devDependencies": { + "@eslint/js": "^9.36.0", + "@types/react": "^19.1.13", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.3", + "eslint": "^9.36.0", + "eslint-config-prettier": "^10.1.8", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.20", + "globals": "^16.4.0", + "prettier": "3.6.2", + "typescript": "~5.8.3", + "typescript-eslint": "^8.44.0", + "vite": "npm:rolldown-vite@7.1.12" + } + }, + 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+ "eslint-config-prettier": "^10.1.8", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.20", + "globals": "^16.4.0", + "prettier": "3.6.2", + "typescript": "~5.8.3", + "typescript-eslint": "^8.44.0", + "vite": "npm:rolldown-vite@7.1.12" + }, + "overrides": { + "vite": "npm:rolldown-vite@7.1.12" + } +} diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx new file mode 100644 index 0000000..35e3655 --- /dev/null +++ b/src/frontend/src/App.tsx @@ -0,0 +1,15 @@ +import { CssBaseline, Stack } from "@mui/material"; +import CalorieTracker from "./components/CalorieTracker"; + +function App() { + return ( + <> + + + + + + ); +} + +export default App; diff --git a/src/frontend/src/api/api.ts b/src/frontend/src/api/api.ts new file mode 100644 index 0000000..a797df0 --- /dev/null +++ b/src/frontend/src/api/api.ts @@ -0,0 +1,23 @@ +export type InitResponse = { sessionId: string }; +export type ChatResponse = { sessionId: string; output: string }; + +const BASE_URL = import.meta.env.VITE_AGENT_URL ?? "http://localhost:8080"; + +export async function initSession(): Promise { + const res = await fetch(`${BASE_URL}/init`, { method: "POST" }); + if (!res.ok) throw new Error(`init failed: ${res.status}`); + return res.json(); +} + +export async function sendMessage( + sessionId: string, + message: string +): Promise { + const res = await fetch(`${BASE_URL}/chat`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ sessionId, message }), + }); + if (!res.ok) throw new Error(`chat failed: ${res.status}`); + return res.json(); +} diff --git a/src/frontend/src/components/CalorieTracker.tsx b/src/frontend/src/components/CalorieTracker.tsx new file mode 100644 index 0000000..9b1c62d --- /dev/null +++ b/src/frontend/src/components/CalorieTracker.tsx @@ -0,0 +1,328 @@ +import { useEffect, useMemo, useState } from "react"; +import { + Box, + Button, + Paper, + Stack, + Step, + StepLabel, + Stepper, + Typography, +} from "@mui/material"; + +import { initSession, sendMessage } from "../api/api"; +import { + type FormState, + type MealEntry, + type Totals, + type PlanData, + initialFormState, + initialTotals, + isStepValid, + steps, + goalOptions, + calculateRemaining, + parsePlanResponse, + loadPersistedState, + savePersistedState, + clearPersistedState, + buildPlanPrompt, + buildMealPrompt, +} from "./calorie-tracker"; +import { FormStep } from "./calorie-tracker/formStep"; +import { PlanCard } from "./calorie-tracker/PlanCard"; +import { MealLogger } from "./calorie-tracker/MealLogger"; +import MealLogContext from "./calorie-tracker/MealLogContext"; +import ChatWidget from "./ChatWidget"; + +export default function CalorieTracker() { + const [sessionId, setSessionId] = useState(null); + const [activeStep, setActiveStep] = useState(0); + const [form, setForm] = useState(initialFormState); + const [planSummary, setPlanSummary] = useState(""); + const [planData, setPlanData] = useState(null); + const [planError, setPlanError] = useState(null); + const [planLoading, setPlanLoading] = useState(false); + const [mealInput, setMealInput] = useState(""); + const [mealLoading, setMealLoading] = useState(false); + const [mealError, setMealError] = useState(null); + const [entries, setEntries] = useState([]); + const [totals, setTotals] = useState(initialTotals); + const [hydrated, setHydrated] = useState(false); + + useEffect(() => { + const persisted = loadPersistedState(); + if (persisted) { + setForm(persisted.form); + setPlanSummary(persisted.planSummary); + setPlanData(persisted.planData); + setTotals(persisted.totals); + setEntries(persisted.entries); + setActiveStep(persisted.activeStep); + } + setHydrated(true); + }, []); + + useEffect(() => { + (async () => { + try { + const init = await initSession(); + setSessionId(init.sessionId); + } catch (error) { + console.error("Failed to init session", error); + } + })(); + }, []); + + useEffect(() => { + if (!hydrated) return; + if (!form.goal) return; + const goalConfig = goalOptions.find((g) => g.value === form.goal); + if (!goalConfig) return; + if (form.weeklyRate.trim() === "" || form.weeklyRate === "0") { + setForm((prev) => ({ ...prev, weeklyRate: goalConfig.defaultRate })); + } + }, [form.goal, form.weeklyRate, hydrated]); + + const stepValid = useMemo( + () => isStepValid(form, activeStep), + [form, activeStep] + ); + + const handleNext = async () => { + if (activeStep < steps.length - 1) { + setActiveStep((prev) => prev + 1); + return; + } + await generatePlan(); + }; + + const generatePlan = async () => { + if (!sessionId) return; + setPlanLoading(true); + setPlanError(null); + setPlanData(null); + setPlanSummary(""); + try { + const prompt = buildPlanPrompt(form); + const res = await sendMessage(sessionId, prompt); + const { summary, data } = parsePlanResponse(res.output); + setPlanSummary(summary); + setPlanData(data); + setTotals(initialTotals); + setEntries([]); + setActiveStep(steps.length); + } catch (error: any) { + console.error(error); + setPlanError(error?.message || "Failed to generate plan"); + } finally { + setPlanLoading(false); + } + }; + + const handleLogMealFromText = async ( + description: string, + fromChat = false + ) => { + if (!sessionId || !planSummary) { + return { + success: false, + message: "Meal logging is not ready yet.", + }; + } + const mealText = description.trim(); + if (!mealText) { + return { success: false, message: "Please provide a meal description." }; + } + + if (!fromChat) { + setMealLoading(true); + setMealError(null); + } + + try { + const prompt = buildMealPrompt(mealText, totals, planSummary); + const res = await sendMessage(sessionId, prompt); + const raw = res.output.trim(); + let estimates: Totals | undefined; + let runningTotals: Totals | undefined; + let notes: string | undefined; + let parseError = false; + try { + const parsed = JSON.parse(raw); + estimates = parsed.estimates; + runningTotals = parsed.running_totals; + notes = parsed.notes; + if (runningTotals && typeof runningTotals.calories === "number") { + setTotals(runningTotals); + } + } catch (error) { + parseError = true; + console.warn("Failed to parse meal response as JSON", error, raw); + } + + setEntries((prev) => [ + { + meal: mealText, + response: raw, + estimates, + runningTotals, + notes, + parseError, + timestamp: new Date().toISOString(), + }, + ...prev, + ]); + + return { + success: true, + message: parseError + ? "Logged meal, but could not parse structured data." + : "Meal logged successfully.", + parseError, + }; + } catch (error: any) { + console.error(error); + return { + success: false, + message: error?.message ?? "Failed to log meal.", + }; + } finally { + if (!fromChat) { + setMealLoading(false); + } + } + }; + + const handleLogMeal = async () => { + if (!mealInput.trim()) return; + const result = await handleLogMealFromText(mealInput, false); + if (result.success) { + setMealInput(""); + } else { + setMealError(result.message ?? "Failed to log meal"); + } + }; + + const remaining = useMemo( + () => calculateRemaining(planData?.targets ?? null, totals), + [planData, totals] + ); + + useEffect(() => { + if (!hydrated) return; + savePersistedState({ + form, + planSummary, + planData, + totals, + entries, + activeStep, + }); + }, [form, planSummary, planData, totals, entries, activeStep, hydrated]); + + return ( + handleLogMealFromText(text, true), + canLogMeal: Boolean(sessionId && planSummary), + }} + > + + + maitre d' + + + Share a few details, review your personalised plan, then log meals in + natural language to keep tabs on calories and macros. + + + + + {steps.map((label) => ( + + {label} + + ))} + + + {activeStep < steps.length && ( + + + setForm((prev) => ({ ...prev, ...fields })) + } + /> + + {activeStep > 0 && ( + + )} + + + {planError && ( + + {planError} + + )} + + )} + + {activeStep >= steps.length && ( + setActiveStep(steps.length - 1)} + onReset={() => { + setPlanSummary(""); + setPlanData(null); + setTotals(initialTotals); + setEntries([]); + setMealInput(""); + setForm(initialFormState); + setActiveStep(0); + clearPersistedState(); + }} + /> + )} + + + {planSummary && ( + + )} + + + + ); +} diff --git a/src/frontend/src/components/Chat.tsx b/src/frontend/src/components/Chat.tsx new file mode 100644 index 0000000..3ac10b7 --- /dev/null +++ b/src/frontend/src/components/Chat.tsx @@ -0,0 +1,104 @@ +import { useEffect, useRef, useState } from "react"; +import { + Box, + Button, + Paper, + Stack, + TextField, + Typography, +} from "@mui/material"; +import { initSession, sendMessage } from "../api/api"; + +type Message = { role: "user" | "assistant"; content: string }; + +export default function Chat() { + const [sessionId, setSessionId] = useState(null); + const [messages, setMessages] = useState([]); + const [input, setInput] = useState(""); + const [busy, setBusy] = useState(false); + const endRef = useRef(null); + + useEffect(() => { + (async () => { + try { + const init = await initSession(); + setSessionId(init.sessionId); + } catch (e) { + console.error(e); + } + })(); + }, []); + + useEffect(() => { + endRef.current?.scrollIntoView({ behavior: "smooth" }); + }, [messages]); + + async function onSend() { + const text = input.trim(); + if (!text || !sessionId) return; + setInput(""); + setMessages((m) => [...m, { role: "user", content: text }]); + setBusy(true); + try { + const res = await sendMessage(sessionId, text); + setSessionId(res.sessionId); + setMessages((m) => [...m, { role: "assistant", content: res.output }]); + } catch (e) { + console.error(e); + setMessages((m) => [ + ...m, + { + role: "assistant", + content: "Sorry, something went wrong. Please try again.", + }, + ]); + } finally { + setBusy(false); + } + } + + return ( + + + Agent Chat + + + + {messages.map((m, i) => ( + + + {m.role === "user" ? "You" : "Assistant"} + + {m.content} + + ))} +
+ + + + setInput(e.target.value)} + onKeyDown={(e) => { + if (e.key === "Enter" && !e.shiftKey) { + e.preventDefault(); + onSend(); + } + }} + /> + + + + ); +} diff --git a/src/frontend/src/components/ChatWidget.tsx b/src/frontend/src/components/ChatWidget.tsx new file mode 100644 index 0000000..7902dcc --- /dev/null +++ b/src/frontend/src/components/ChatWidget.tsx @@ -0,0 +1,222 @@ +import { useEffect, useRef, useState } from "react"; +import { + Box, + Fab, + IconButton, + Stack, + TextField, + Typography, + CircularProgress, +} from "@mui/material"; +import { FiMessageSquare, FiX } from "react-icons/fi"; + +import { initSession, sendMessage } from "../api/api"; +import { useMealLog } from "./calorie-tracker"; + +type Message = { role: "user" | "assistant"; content: string }; + +export default function ChatWidget() { + const [open, setOpen] = useState(false); + const [sessionId, setSessionId] = useState(null); + const [messages, setMessages] = useState([]); + const [input, setInput] = useState(""); + const [busy, setBusy] = useState(false); + const scrollRef = useRef(null); + + const { logMealFromText, canLogMeal } = useMealLog(); + + useEffect(() => { + if (!open || sessionId) return; + (async () => { + try { + const init = await initSession(); + setSessionId(init.sessionId); + setMessages([ + { + role: "assistant", + content: + "Hi! I'm your AI assistant. Ask me anything about your nutrition plan or meals.", + }, + ]); + } catch (error) { + console.error("Failed to initialise chat session", error); + } + })(); + }, [open, sessionId]); + + useEffect(() => { + scrollRef.current?.scrollIntoView({ behavior: "smooth" }); + }, [messages, open]); + + const handleToggle = () => { + setOpen((prev) => !prev); + }; + + const handleSend = async () => { + const text = input.trim(); + if (!text || !sessionId) return; + + setInput(""); + setMessages((prev) => [...prev, { role: "user", content: text }]); + setBusy(true); + try { + const systemIntro = canLogMeal + ? 'If the user explicitly describes a meal they consumed today, respond with both a natural language confirmation AND a JSON snippet formatted as ```json { "action": "log_meal", "meal": "..." } ```.' + : "Answer conversationally."; + const res = await sendMessage(sessionId, `${systemIntro}\nUser: ${text}`); + const output = res.output.trim(); + setMessages((prev) => [...prev, { role: "assistant", content: output }]); + + if (canLogMeal) { + const match = output.match(/```json([\s\S]*?)```/); + if (match) { + try { + const parsed = JSON.parse(match[1].trim()); + if (parsed?.action === "log_meal" && parsed.meal) { + const result = await logMealFromText(parsed.meal as string); + if (!result.success) { + setMessages((prev) => [ + ...prev, + { + role: "assistant", + content: + result.message || + "I couldn't log that meal automatically. Try entering it from the tracker.", + }, + ]); + } + } + } catch (error) { + console.warn("Failed to parse JSON action", error); + } + } + } + } catch (error) { + console.error(error); + setMessages((prev) => [ + ...prev, + { + role: "assistant", + content: "Sorry, something went wrong. Please try again.", + }, + ]); + } finally { + setBusy(false); + } + }; + + return ( + <> + + {open ? : } + + + {open && ( + + + + Chat with AI + + + + + + + + {messages.map((msg, idx) => ( + + {msg.content} + + ))} + {busy && ( + + + Thinking... + + )} +
+ + + + setInput(e.target.value)} + onKeyDown={(e) => { + if (e.key === "Enter" && !e.shiftKey) { + e.preventDefault(); + handleSend(); + } + }} + disabled={busy || !sessionId} + /> + + + )} + + ); +} diff --git a/src/frontend/src/components/calorie-tracker/MealLogContext.tsx b/src/frontend/src/components/calorie-tracker/MealLogContext.tsx new file mode 100644 index 0000000..809dbfe --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/MealLogContext.tsx @@ -0,0 +1,26 @@ +import { createContext, useContext } from "react"; + +export type MealLogResult = { + success: boolean; + message?: string; + parseError?: boolean; +}; + +export type MealLogContextValue = { + logMealFromText: (description: string) => Promise; + canLogMeal: boolean; +}; + +const MealLogContext = createContext({ + logMealFromText: async () => ({ + success: false, + message: "Meal logging is not available right now.", + }), + canLogMeal: false, +}); + +export function useMealLog(): MealLogContextValue { + return useContext(MealLogContext); +} + +export default MealLogContext; diff --git a/src/frontend/src/components/calorie-tracker/MealLogger.tsx b/src/frontend/src/components/calorie-tracker/MealLogger.tsx new file mode 100644 index 0000000..addced6 --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/MealLogger.tsx @@ -0,0 +1,130 @@ +import { + Alert, + Button, + Paper, + Stack, + TextField, + Typography, +} from "@mui/material"; +import { type Totals, type MealEntry } from "./types"; + +function formatTotals({ calories, protein_g, carbs_g, fat_g }: Totals) { + return `Calories: ${calories.toFixed(1)} kcal · Protein: ${protein_g.toFixed(1)} g · Carbs: ${carbs_g.toFixed(1)} g · Fat: ${fat_g.toFixed(1)} g`; +} + +function formatRemaining(totals: Totals) { + return `Calories: ${Math.max(0, totals.calories).toFixed(1)} kcal · Protein: ${Math.max(0, totals.protein_g).toFixed(1)} g · Carbs: ${Math.max(0, totals.carbs_g).toFixed(1)} g · Fat: ${Math.max(0, totals.fat_g).toFixed(1)} g`; +} + +type MealLoggerProps = { + planSummary: string; + totals: Totals; + entries: MealEntry[]; + mealInput: string; + onInputChange: (value: string) => void; + onLogMeal: () => void; + loading: boolean; + error: string | null; + remaining?: Totals | null; +}; + +export function MealLogger({ + planSummary, + totals, + entries, + mealInput, + onInputChange, + onLogMeal, + loading, + error, + remaining, +}: MealLoggerProps) { + return ( + + + Log a meal + + + Describe what you ate in plain language. The AI will estimate calories + and macros using the food MCP and keep track of totals. + + + onInputChange(e.target.value)} + disabled={!planSummary} + /> + + + {error && ( + + {error} + + )} + + + Running totals + {formatTotals(totals)} + {remaining && ( + + Remaining today: {formatRemaining(remaining)} + + )} + + + + {entries.length === 0 && ( + No meals logged yet. + )} + {entries.map((entry) => ( + + {entry.meal} + {entry.estimates && entry.runningTotals && !entry.parseError ? ( + + + Estimated: {entry.estimates.calories.toFixed(1)} kcal · + Protein {entry.estimates.protein_g.toFixed(1)} g · Carbs{" "} + {entry.estimates.carbs_g.toFixed(1)} g · Fat{" "} + {entry.estimates.fat_g.toFixed(1)} g + + + New totals: {entry.runningTotals.calories.toFixed(1)} kcal · + Protein {entry.runningTotals.protein_g.toFixed(1)} g · Carbs{" "} + {entry.runningTotals.carbs_g.toFixed(1)} g · Fat{" "} + {entry.runningTotals.fat_g.toFixed(1)} g + + {entry.notes && ( + Notes: {entry.notes} + )} + + ) : ( + + {entry.response} + + )} + {entry.parseError && ( + + Could not parse structured response; showing raw output. + + )} + + ))} + + + ); +} diff --git a/src/frontend/src/components/calorie-tracker/PlanCard.tsx b/src/frontend/src/components/calorie-tracker/PlanCard.tsx new file mode 100644 index 0000000..ad30fbd --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/PlanCard.tsx @@ -0,0 +1,54 @@ +import { + Alert, + Button, + CircularProgress, + Paper, + Stack, + Typography, +} from "@mui/material"; + +type PlanCardProps = { + plan: string; + loading: boolean; + error: string | null; + onBackToEdit: () => void; + onReset: () => void; +}; + +export function PlanCard({ + plan, + loading, + error, + onBackToEdit, + onReset, +}: PlanCardProps) { + return ( + + Your personalised plan + {loading ? ( + + + Creating your plan… + + ) : ( + + {plan} + + )} + {error && {error}} + + + + + + ); +} diff --git a/src/frontend/src/components/calorie-tracker/formStep.tsx b/src/frontend/src/components/calorie-tracker/formStep.tsx new file mode 100644 index 0000000..b0227d3 --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/formStep.tsx @@ -0,0 +1,126 @@ +import { MenuItem, Stack, TextField, Typography } from "@mui/material"; + +import { + type FormState, + activityLevels, + goalOptions, + type GoalValue, +} from "./types"; + +type FormStepProps = { + stepIndex: number; + form: FormState; + onChange: (fields: Partial) => void; +}; + +export function FormStep({ stepIndex, form, onChange }: FormStepProps) { + if (stepIndex === 0) { + return ( + + Tell us about yourself + onChange({ name: e.target.value })} + /> + + onChange({ sex: e.target.value as FormState["sex"] }) + } + > + Male + Female + Non-binary + + onChange({ ageYears: e.target.value })} + /> + + ); + } + + if (stepIndex === 1) { + return ( + + Measurements + onChange({ heightCm: e.target.value })} + /> + onChange({ weightKg: e.target.value })} + /> + + ); + } + + return ( + + Lifestyle & goal + onChange({ activityLevel: e.target.value })} + > + {activityLevels.map((option) => ( + + {option.label} + + ))} + + onChange({ goal: e.target.value as GoalValue })} + > + {goalOptions.map((goal) => ( + + {goal.label} + + ))} + + {form.goal && ( + + {goalOptions.find((g) => g.value === form.goal)?.helper || + "Choose a comfortable pace"} + + )} + onChange({ weeklyRate: e.target.value })} + helperText="Positive for gain, negative for loss." + /> + onChange({ dietaryNotes: e.target.value })} + /> + + ); +} diff --git a/src/frontend/src/components/calorie-tracker/index.ts b/src/frontend/src/components/calorie-tracker/index.ts new file mode 100644 index 0000000..c658953 --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/index.ts @@ -0,0 +1,8 @@ +export * from "./types"; +export * from "./formStep"; +export * from "./PlanCard"; +export * from "./MealLogger"; +export * from "./utils"; +export * from "./persistence"; +export * from "./prompts"; +export * from "./MealLogContext"; diff --git a/src/frontend/src/components/calorie-tracker/persistence.ts b/src/frontend/src/components/calorie-tracker/persistence.ts new file mode 100644 index 0000000..9d7040f --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/persistence.ts @@ -0,0 +1,55 @@ +import { + type FormState, + type MealEntry, + type Totals, + type PlanData, + initialFormState, + initialTotals, +} from "./types"; + +const STORAGE_KEY = "ai-calorie-tracker-state"; + +export type PersistedTrackerState = { + form: FormState; + planSummary: string; + planData: PlanData | null; + totals: Totals; + entries: MealEntry[]; + activeStep: number; +}; + +export function loadPersistedState(): PersistedTrackerState | null { + try { + const raw = localStorage.getItem(STORAGE_KEY); + if (!raw) return null; + const parsed = JSON.parse(raw); + if (!parsed || typeof parsed !== "object") return null; + return { + form: parsed.form ?? initialFormState, + planSummary: parsed.planSummary ?? "", + planData: parsed.planData ?? null, + totals: parsed.totals ?? initialTotals, + entries: Array.isArray(parsed.entries) ? parsed.entries : [], + activeStep: typeof parsed.activeStep === "number" ? parsed.activeStep : 0, + } satisfies PersistedTrackerState; + } catch (error) { + console.warn("Failed to load persisted tracker state", error); + return null; + } +} + +export function savePersistedState(state: PersistedTrackerState) { + try { + localStorage.setItem(STORAGE_KEY, JSON.stringify(state)); + } catch (error) { + console.warn("Failed to persist tracker state", error); + } +} + +export function clearPersistedState() { + try { + localStorage.removeItem(STORAGE_KEY); + } catch (error) { + console.warn("Failed to clear tracker state", error); + } +} diff --git a/src/frontend/src/components/calorie-tracker/prompts.ts b/src/frontend/src/components/calorie-tracker/prompts.ts new file mode 100644 index 0000000..071b8b2 --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/prompts.ts @@ -0,0 +1,85 @@ +import { type FormState, type Totals } from "./types"; + +export function buildPlanPrompt(form: FormState) { + return `You are an AI nutrition coach. Use the nutritional_plan compose_plan tool to calculate daily calories and macronutrients for this user. + + User profile: + - Name: ${form.name || "N/A"} + - Sex: ${form.sex} + - Age: ${form.ageYears} + - Height: ${form.heightCm} cm + - Weight: ${form.weightKg} kg + - Activity level: ${form.activityLevel} + - Goal: ${form.goal} + - Weekly rate target (kg/week): ${form.weeklyRate || "0"} + - Dietary notes: ${form.dietaryNotes || "none"} + + Return your answer in the following exact format: + + ### Daily Nutrition Plan + (Provide 2–3 short paragraphs summarising the plan and highlighting key advice. Use the nutrition_plan compose_plan tool to generate the plan.) + + #### Daily Targets + - Calories: kcal + - Protein: g + - Carbs: g + - Fat: g + + #### Macro Split + - Protein: % + - Carbs: % + - Fat: % + + #### Guidance + - Bullet point 1 + - Bullet point 2 + - Bullet point 3 + + \`\`\`json + { + "targets": { + "calories": , + "protein_g": , + "carbs_g": , + "fat_g": + }, + "macro_percentages": { + "protein": , + "carbs": , + "fat": + }, + "guidance": ["string", "string", "string"] + } + \`\`\` + + Rules: + - Use plain digits without thousands separators in the JSON values (e.g. 2050, not 2,050). + - Ensure the JSON block is valid parseable JSON and matches the schema exactly. + - Do not add any other text after the closing \`\`\` fence.`; +} + +export function buildMealPrompt( + mealDescription: string, + totals: Totals, + planSummary: string +) { + return `We already created the following nutrition plan: +${planSummary} + +Current running totals (kcal, protein_g, carbs_g, fat_g): ${JSON.stringify( + totals + )} + +The user just consumed: +"""${mealDescription}""" + +Use the macros analyze_macros/analyze_recipe tools to estimate this meal. +Return JSON ONLY using this schema (no code fences): +{ + "meal": string, + "estimates": {"calories": number, "protein_g": number, "carbs_g": number, "fat_g": number}, + "running_totals": {"calories": number, "protein_g": number, "carbs_g": number, "fat_g": number}, + "notes": string +} +Round numbers to 1 decimal place.`; +} diff --git a/src/frontend/src/components/calorie-tracker/types.ts b/src/frontend/src/components/calorie-tracker/types.ts new file mode 100644 index 0000000..799e6ee --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/types.ts @@ -0,0 +1,135 @@ +export type SexOption = "male" | "female" | "nonbinary" | ""; +export type GoalValue = "lose_weight" | "maintain" | "gain_muscle" | ""; + +export type FormState = { + name: string; + sex: SexOption; + ageYears: string; + heightCm: string; + weightKg: string; + activityLevel: string; + goal: GoalValue; + weeklyRate: string; + dietaryNotes: string; +}; + +export type Totals = { + calories: number; + protein_g: number; + carbs_g: number; + fat_g: number; +}; + +export type PlanData = { + targets: Totals; + guidance?: string[]; + macroPercentages?: { + protein?: number; + carbs?: number; + fat?: number; + }; +}; + +export type MealEntry = { + meal: string; + response: string; + estimates?: Totals; + runningTotals?: Totals; + notes?: string; + parseError?: boolean; + timestamp: string; +}; + +export type ActivityLevelOption = { + value: string; + label: string; +}; + +export type GoalOption = { + value: Exclude; + label: string; + defaultRate: string; + helper: string; +}; + +export const steps = [ + "Basic info", + "Measurements", + "Lifestyle & goal", +] as const; + +export const activityLevels: ActivityLevelOption[] = [ + { value: "sedentary", label: "Sedentary – little or no exercise" }, + { value: "lightly_active", label: "Lightly active – 1-3 days exercise" }, + { value: "moderately_active", label: "Moderately active – 3-5 days" }, + { value: "very_active", label: "Very active – 6-7 days" }, + { value: "extra_active", label: "Extra active – physical job or training" }, +]; + +export const goalOptions: GoalOption[] = [ + { + value: "lose_weight", + label: "Lose weight", + defaultRate: "-0.4", + helper: "Typical 0.25–0.7 kg per week", + }, + { + value: "maintain", + label: "Maintain weight", + defaultRate: "0", + helper: "Stay around current weight", + }, + { + value: "gain_muscle", + label: "Gain muscle", + defaultRate: "0.25", + helper: "Typical 0.1–0.4 kg per week", + }, +]; + +export const initialFormState: FormState = { + name: "", + sex: "", + ageYears: "", + heightCm: "", + weightKg: "", + activityLevel: "", + goal: "", + weeklyRate: "", + dietaryNotes: "", +}; + +export const initialTotals: Totals = { + calories: 0, + protein_g: 0, + carbs_g: 0, + fat_g: 0, +}; + +export function isStepValid(form: FormState, stepIndex: number): boolean { + if (stepIndex === 0) { + return Boolean(form.sex && Number(form.ageYears) > 0); + } + if (stepIndex === 1) { + return Number(form.heightCm) > 0 && Number(form.weightKg) > 0; + } + if (stepIndex === 2) { + if (!form.activityLevel || !form.goal) { + return false; + } + if (form.goal !== "maintain") { + return form.weeklyRate === "" || !Number.isNaN(Number(form.weeklyRate)); + } + return true; + } + return true; +} + +export function subtractTotals(target: Totals, consumed: Totals): Totals { + return { + calories: target.calories - consumed.calories, + protein_g: target.protein_g - consumed.protein_g, + carbs_g: target.carbs_g - consumed.carbs_g, + fat_g: target.fat_g - consumed.fat_g, + }; +} diff --git a/src/frontend/src/components/calorie-tracker/utils.ts b/src/frontend/src/components/calorie-tracker/utils.ts new file mode 100644 index 0000000..9f35a54 --- /dev/null +++ b/src/frontend/src/components/calorie-tracker/utils.ts @@ -0,0 +1,39 @@ +import { type Totals, type PlanData } from "./types"; + +export function calculateRemaining( + target: Totals | null, + consumed: Totals +): Totals | null { + if (!target) return null; + return { + calories: target.calories - consumed.calories, + protein_g: target.protein_g - consumed.protein_g, + carbs_g: target.carbs_g - consumed.carbs_g, + fat_g: target.fat_g - consumed.fat_g, + }; +} + +export function parsePlanResponse(output: string): { + summary: string; + data: PlanData; +} { + const delimiter = "```json"; + const start = output.indexOf(delimiter); + if (start === -1) { + throw new Error("Plan response missing JSON section"); + } + const summary = output.slice(0, start).trim(); + const jsonPart = output.slice(start + delimiter.length); + const closingIndex = jsonPart.indexOf("```", 0); + const jsonString = ( + closingIndex === -1 ? jsonPart : jsonPart.slice(0, closingIndex) + ).trim(); + const parsed = JSON.parse(jsonString); + if (!parsed?.targets) { + throw new Error("Plan response JSON missing targets"); + } + return { + summary, + data: parsed, + }; +} diff --git a/src/frontend/src/index.css b/src/frontend/src/index.css new file mode 100644 index 0000000..355335f --- /dev/null +++ b/src/frontend/src/index.css @@ -0,0 +1,4 @@ +:root { + height: 100dvh; + width: 100dvw; +} \ No newline at end of file diff --git a/src/frontend/src/main.tsx b/src/frontend/src/main.tsx new file mode 100644 index 0000000..1f9a37b --- /dev/null +++ b/src/frontend/src/main.tsx @@ -0,0 +1,12 @@ +import { StrictMode } from "react"; +import { createRoot } from "react-dom/client"; +import "./index.css"; +import App from "./App.tsx"; +import { CssBaseline } from "@mui/material"; + +createRoot(document.getElementById("root")!).render( + + + + +); diff --git a/src/frontend/tsconfig.app.json b/src/frontend/tsconfig.app.json new file mode 100644 index 0000000..a9b5a59 --- /dev/null +++ b/src/frontend/tsconfig.app.json @@ -0,0 +1,28 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + "target": "ES2022", + "useDefineForClassFields": true, + "lib": ["ES2022", "DOM", "DOM.Iterable"], + "module": "ESNext", + "types": ["vite/client"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["src"] +} diff --git a/src/frontend/tsconfig.json b/src/frontend/tsconfig.json new file mode 100644 index 0000000..1ffef60 --- /dev/null +++ b/src/frontend/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/src/frontend/tsconfig.node.json b/src/frontend/tsconfig.node.json new file mode 100644 index 0000000..7a77bab --- /dev/null +++ b/src/frontend/tsconfig.node.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + "target": "ES2023", + "lib": ["ES2023"], + "module": "ESNext", + "types": [], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/src/frontend/vite.config.ts b/src/frontend/vite.config.ts new file mode 100644 index 0000000..8b0f57b --- /dev/null +++ b/src/frontend/vite.config.ts @@ -0,0 +1,7 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react()], +}) diff --git a/src/mcp/attractions-mcp/README.md b/src/mcp/attractions-mcp/README.md index 5af9573..7ad4545 100644 --- a/src/mcp/attractions-mcp/README.md +++ b/src/mcp/attractions-mcp/README.md @@ -5,18 +5,21 @@ A Model Context Protocol (MCP) server for discovering and booking tourist attrac ## Features 🏛️ **Attraction Discovery** + - Search attractions by location and category - Get detailed attraction information - Discover random famous attractions - Explore world wonders 🎫 **Booking System** + - Book attraction visits - Generate confirmation codes - Calculate estimated costs - Validate visit dates and requirements 🗂️ **Categories Supported** + - Historical Sites - Natural Wonders - Cultural Sites @@ -46,37 +49,46 @@ uv run mcp dev main.py ``` ```bash -# use this when wanting to consume within an agent +# use this when wanting to consume within an agent (HTTP on port 8008) uv run main.py ``` ### Available Tools #### 1. Get Attraction Details + ```python get_attraction_details(attraction_id: int) ``` + Get comprehensive information about a specific attraction including facilities, best visiting times, and reviews. #### 2. Search Attractions + ```python search_attractions(location: str = None, category: str = None, limit: int = 20) ``` + Search for attractions with optional location and category filters. #### 3. Random Attraction Discovery + ```python get_random_attraction(region: str = "famous") ``` + Get a random attraction for inspiration. Use `region="india"` for Indian attractions. #### 4. World Wonders + ```python get_world_wonders() ``` + Get the list of world wonder attractions. #### 5. Book Attraction + ```python book_attraction( attraction_id: int, @@ -88,23 +100,29 @@ book_attraction( special_requirements: str = None ) ``` + Book a visit to an attraction with confirmation. #### 6. Get Categories + ```python get_attraction_categories() ``` + Get all available attraction categories for filtering. #### 7. Search and Format + ```python search_and_format_attractions(location: str = None, category: str = None, limit: int = 10) ``` + Search attractions and return nicely formatted results. ### Resources Access attraction data as resources: + - `attraction://{attraction_id}` - Specific attraction details - `attractions://search/{location}` - Attractions by location - `attractions://category/{category}` - Attractions by category @@ -128,7 +146,7 @@ get_attraction_details(123) book_attraction( attraction_id=123, visitor_name="John Doe", - email="john@example.com", + email="john@example.com", visit_date="2024-06-15", num_visitors=2 ) @@ -154,6 +172,7 @@ src/mcp/attractions-mcp/ ## Development The codebase follows a modular structure similar to the weather-mcp: + - **Models**: Data structures for attractions and bookings - **Config**: Mock Data for attractions - **Utils**: Helper functions for API calls and validation diff --git a/src/mcp/datastore-mcp/.data/store.journal.msgpack b/src/mcp/datastore-mcp/.data/store.journal.msgpack new file mode 100644 index 0000000..0a0b54e Binary files /dev/null and b/src/mcp/datastore-mcp/.data/store.journal.msgpack differ diff --git a/src/mcp/datastore-mcp/README.md b/src/mcp/datastore-mcp/README.md new file mode 100644 index 0000000..651080c --- /dev/null +++ b/src/mcp/datastore-mcp/README.md @@ -0,0 +1,80 @@ +# Datastore MCP Server + +Persistent, token-efficient, namespaced key-value storage for MCP agents. + +## Features +- Namespaces to segment data logically +- Arbitrary (MessagePack-serializable) values: primitives, lists, dicts, small vectors +- Metadata per record (e.g., source, tags) +- Incrementing revisions per key +- Append-only journal + compact snapshot for durability +- Tools for CRUD + dump + stats + compaction +- Resource URIs for quick retrieval: `memory://{namespace}/{key}` and `memory://{namespace}` +- Prompt helper to summarize a namespace subset + +## File Format +- Snapshot (`store.snapshot.msgpack`): msgpack map `{ namespace -> { key -> {"v": value, "t": ts, "rev": int, "m"?: metadata} } }` +- Journal (`store.journal.msgpack`): sequence of msgpack objects: + - Write: `{"op":1,"ns":str,"k":str,"v":any,"t":int,"m"?:dict,"rev":int}` + - Delete: `{"op":0,"ns":str,"k":str,"rev":int}` + +Short property names reduce serialized size and improve token efficiency if later surfaced to an LLM. + +## Tools +| Tool | Description | +|------|-------------| +| `save_data(key, data, namespace='default', metadata=None, replace=True)` | Store/replace a value | +| `read_data(key, namespace='default')` | Retrieve one record | +| `list_keys(namespace='default')` | List keys in a namespace | +| `delete_data(key, namespace='default')` | Remove a key | +| `dump_namespace(namespace='default', limit=None, include_metadata=False)` | Bulk read subset | +| `compact_store()` | Rewrite snapshot & clear journal | +| `stats()` | Storage statistics | + +## Resources +- `memory://{namespace}/{key}` → compact JSON single record +- `memory://{namespace}` → namespace summary (keys truncated to 50) + +## Prompt +`memory_summary_prompt(namespace='default', objective=None, limit=10)` returns a concise textual overview helpful for grounding a model. + +## Example Flow +```python +# Save +save_data(key="conversation_1", data={"messages": ["hi", "hello"], "summary": "greeting"}) +# Read +read_data(key="conversation_1") +# List +list_keys(namespace="default") +# Dump first 5 +dump_namespace(limit=5) +# Compact +compact_store() +# Stats +stats() +``` + +## Token Efficiency Notes +- MessagePack avoids verbose JSON quoting; internal management uses very short keys. +- Use `dump_namespace` with a limit for summarization; avoid asking the model to ingest the entire store. +- Metadata is optional—omit when not necessary. +- Store embeddings or large blobs sparingly; consider external vector DB for high-dimensional data. + +## Compaction Strategy +- Journal is append-only for durability. +- Call `compact_store` occasionally (e.g., after N writes) to reduce disk size and replay time. + +## Future Enhancements (ideas) +- Optional zstd compression layer +- TTL / expiration per record +- File-lock for multi-process safety +- Query by metadata indexes + +## Running +From this directory: +``` +uv run mcp dev main.py +``` + +## License +Internal hackathon component; adapt as needed. diff --git a/src/mcp/datastore-mcp/main.py b/src/mcp/datastore-mcp/main.py new file mode 100644 index 0000000..5647041 --- /dev/null +++ b/src/mcp/datastore-mcp/main.py @@ -0,0 +1,161 @@ +"""Datastore MCP Server - Persistent token-efficient namespaced key-value storage. + +Run: + uv run mcp dev main.py +""" +from typing import Any, Dict, Optional +import json +import os + +from mcp.server.fastmcp import FastMCP + +from storage import DataStore + +DATA_DIR = os.path.join(os.path.dirname(__file__), ".data") + +datastore = DataStore(DATA_DIR) + +mcp = FastMCP("Datastore", port=8012) + +# -------------------- Tools -------------------- +@mcp.tool() +def save_data(key: str, data: Any, namespace: str = "default", metadata: Optional[Dict[str, Any]] = None, replace: bool = True) -> Dict[str, Any]: + """Save a value under a namespaced key. + + Args: + key: Identifier within namespace. + data: Any JSON-serializable / msgpack-serializable value. + namespace: Logical grouping (default 'default'). + metadata: Optional small dict for context. + replace: If False and key exists, returns error. + """ + return datastore.save(key, data, namespace, metadata, replace) + + +@mcp.tool() +def read_data(key: str, namespace: str = "default") -> Dict[str, Any]: + """Read a stored record.""" + return datastore.read(key, namespace) + + +@mcp.tool() +def list_keys(namespace: str = "default") -> Dict[str, Any]: + """List keys in a namespace.""" + return datastore.list_keys(namespace) + + +@mcp.tool() +def delete_data(key: str, namespace: str = "default") -> Dict[str, Any]: + """Delete a key from a namespace.""" + return datastore.delete(key, namespace) + + +@mcp.tool() +def dump_namespace(namespace: str = "default", limit: Optional[int] = None, include_metadata: bool = False) -> Dict[str, Any]: + """Return multiple items from a namespace. + + Args: + namespace: Namespace name + limit: Max number of items (None = all) + include_metadata: Include metadata if available + """ + return datastore.dump_namespace(namespace, limit, include_metadata) + + +@mcp.tool() +def compact_store() -> Dict[str, Any]: + """Force compaction (rewrite snapshot & clear journal).""" + return datastore.compact() + + +@mcp.tool() +def stats() -> Dict[str, Any]: + """Return datastore statistics.""" + return datastore.stats() + +# -------------------- Resources -------------------- +@mcp.resource("memory://{namespace}/{key}") +def memory_key_resource(namespace: str, key: str) -> str: + rec = datastore.read(key, namespace) + if "error" in rec: + return f"Memory entry not found: {namespace}/{key}\n" + # Compact JSON representation + payload = {"ns": namespace, "k": key, "v": rec.get("value"), "t": rec.get("timestamp"), "rev": rec.get("rev")} + if "metadata" in rec: + payload["m"] = rec["metadata"] + return json.dumps(payload, separators=(",", ":")) + + +@mcp.resource("memory://{namespace}") +def memory_namespace_resource(namespace: str) -> str: + listing = datastore.list_keys(namespace) + keys = listing.get("keys", []) + summary = {"namespace": namespace, "count": listing.get("count", 0), "keys": keys[:50]} + return json.dumps(summary, separators=(",", ":")) + +# -------------------- Prompt -------------------- +@mcp.prompt() +def memory_summary_prompt(namespace: str = "default", objective: Optional[str] = None, limit: int = 10) -> str: + dump = datastore.dump_namespace(namespace, limit) + lines = [] + if objective: + lines.append(f"Objective: {objective}") + lines.append(f"Namespace '{namespace}' sample (limit={limit}):") + for item in dump.get("items", []): + val = item.get("value") + # Truncate long string values for prompt friendliness + if isinstance(val, str) and len(val) > 200: + val = val[:197] + "..." + lines.append(f"- {item['key']}: {val}") + lines.append("Use these stored items as context; retrieve more with tools if needed.") + return "\n".join(lines) + +@mcp.prompt() +def retrieve_data_prompt( + user_request: str, + namespace: str = "default", + today_iso: Optional[str] = None +) -> str: + """ + Guide the model to decide which datastore operations to perform + to satisfy a retrieval-style user request (e.g., 'today's meal plan'). + + user_request: The original natural language query from the user. + namespace: Namespace to inspect. + today_iso: Pass in today's date (YYYY-MM-DD) so the model doesn't hallucinate. + strategy: Retrieval heuristic name (future extension). + """ + listing = datastore.list_keys(namespace) + keys = listing.get("keys", []) + lines = [] + lines.append("You are planning data retrieval from a persistent key-value store.") + lines.append(f"User request: {user_request}") + if today_iso: + lines.append(f"Today's date (ISO): {today_iso}") + lines.append(f"Namespace: {namespace}") + lines.append(f"Available key count: {listing.get('count', 0)}") + # Show a limited slice only (avoid huge prompt) + preview = keys[:100] + lines.append("Keys:") + for k in preview: + lines.append(f"- {k}") + if len(keys) > len(preview): + lines.append(f"... ({len(keys) - len(preview)} more not shown)") + lines.append("") + lines.append("Retrieval Guidelines:") + lines.append("1. Derive candidate key names from the user request (e.g., date-based).") + lines.append("2. If an exact key match exists, plan to call: read_data(key=, namespace=).") + lines.append("3. If multiple partial matches (e.g. all keys containing today's date), pick the most specific; if unsure, call dump_namespace with a limit.") + lines.append("4. If NO relevant key, respond with PLAN:NOT_FOUND (do not fabricate data).") + lines.append("5. Do NOT invent keys; only use keys listed above.") + lines.append("") + lines.append("Respond ONLY with a PLAN block in one of these forms:") + lines.append("PLAN:READ key= namespace=") + lines.append("PLAN:DUMP namespace= limit=<=20") + lines.append("PLAN:NOT_FOUND") + lines.append("") + lines.append("Do not include any other explanatory text.") + return '\\n'.join(lines) + +if __name__ == "__main__": + mcp.run("streamable-http") diff --git a/src/mcp/datastore-mcp/pyproject.toml b/src/mcp/datastore-mcp/pyproject.toml new file mode 100644 index 0000000..94047b4 --- /dev/null +++ b/src/mcp/datastore-mcp/pyproject.toml @@ -0,0 +1,10 @@ +[project] +name = "datastore-mcp" +version = "0.1.0" +description = "Local namespaced key-value datastore MCP server with token-efficient persistence (MessagePack)." +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mcp[cli]>=1.14.1", + "msgpack", +] diff --git a/src/mcp/datastore-mcp/storage.py b/src/mcp/datastore-mcp/storage.py new file mode 100644 index 0000000..750aa5b --- /dev/null +++ b/src/mcp/datastore-mcp/storage.py @@ -0,0 +1,158 @@ +import os +import time +import threading +import msgpack +from typing import Any, Dict, Optional, Tuple + +# In-memory structure: { namespace: { key: record_dict } } +# record_dict: {"v": value, "t": timestamp(int), "m": metadata(optional dict), "rev": int } + +class DataStore: + def __init__(self, base_dir: str, snapshot_name: str = "store.snapshot.msgpack", journal_name: str = "store.journal.msgpack"): + self.base_dir = base_dir + self.snapshot_path = os.path.join(base_dir, snapshot_name) + self.journal_path = os.path.join(base_dir, journal_name) + self._lock = threading.Lock() + self._data: Dict[str, Dict[str, Dict[str, Any]]] = {} + self._revisions: Dict[Tuple[str, str], int] = {} + self._dirty = False + self._last_compaction: Optional[int] = None + self._corrupt_entries = 0 + os.makedirs(base_dir, exist_ok=True) + self._load() + + # -------------- Persistence -------------- + def _load(self): + # Load snapshot + if os.path.exists(self.snapshot_path): + try: + with open(self.snapshot_path, 'rb') as f: + snapshot = msgpack.unpackb(f.read(), raw=False) + if isinstance(snapshot, dict): + self._data = snapshot + for ns, mapping in self._data.items(): + for k, rec in mapping.items(): + self._revisions[(ns, k)] = int(rec.get("rev", 0)) + except Exception: + # Corrupted snapshot -> start empty but keep file for debugging + self._data = {} + # Replay journal + if os.path.exists(self.journal_path): + try: + with open(self.journal_path, 'rb') as f: + unpacker = msgpack.Unpacker(f, raw=False) + for entry in unpacker: + try: + if not isinstance(entry, dict): + self._corrupt_entries += 1 + continue + op = entry.get("op") + ns = entry.get("ns") + k = entry.get("k") + if op == 1: # write + v = entry.get("v") + t = entry.get("t") + m = entry.get("m") + rev = int(entry.get("rev", 0)) + self._data.setdefault(ns, {})[k] = {"v": v, "t": t, "rev": rev} + if m is not None: + self._data[ns][k]["m"] = m + self._revisions[(ns, k)] = rev + elif op == 0: # delete + if ns in self._data and k in self._data[ns]: + del self._data[ns][k] + self._revisions[(ns, k)] = int(entry.get("rev", 0)) + except Exception: + self._corrupt_entries += 1 + continue + except Exception: + self._corrupt_entries += 1 + + def _append_journal(self, entry: Dict[str, Any]): + with open(self.journal_path, 'ab') as f: + f.write(msgpack.packb(entry, use_bin_type=True)) + + def compact(self) -> Dict[str, Any]: + with self._lock: + tmp_path = self.snapshot_path + ".tmp" + with open(tmp_path, 'wb') as f: + f.write(msgpack.packb(self._data, use_bin_type=True)) + os.replace(tmp_path, self.snapshot_path) + # Reset journal + open(self.journal_path, 'wb').close() + self._last_compaction = int(time.time()) + snapshot_bytes = os.path.getsize(self.snapshot_path) + journal_bytes = os.path.getsize(self.journal_path) + return {"ok": True, "snapshot_bytes": snapshot_bytes, "journal_bytes": journal_bytes, "last_compaction": self._last_compaction} + + # -------------- CRUD Operations -------------- + def save(self, key: str, value: Any, namespace: str = "default", metadata: Optional[Dict[str, Any]] = None, replace: bool = True) -> Dict[str, Any]: + ts = int(time.time()) + with self._lock: + ns_map = self._data.setdefault(namespace, {}) + if not replace and key in ns_map: + return {"error": "exists"} + prev_rev = self._revisions.get((namespace, key), 0) + rev = prev_rev + 1 + rec = {"v": value, "t": ts, "rev": rev} + if metadata: + rec["m"] = metadata + ns_map[key] = rec + self._revisions[(namespace, key)] = rev + self._append_journal({"op": 1, "ns": namespace, "k": key, "v": value, "t": ts, "m": metadata, "rev": rev}) + return {"ok": True, "rev": rev, "timestamp": ts} + + def read(self, key: str, namespace: str = "default") -> Dict[str, Any]: + with self._lock: + rec = self._data.get(namespace, {}).get(key) + if rec is None: + return {"error": "not_found"} + out = {"key": key, "namespace": namespace, "value": rec["v"], "timestamp": rec["t"], "rev": rec.get("rev", 0)} + if "m" in rec: + out["metadata"] = rec["m"] + return out + + def list_keys(self, namespace: str = "default") -> Dict[str, Any]: + with self._lock: + ns_map = self._data.get(namespace, {}) + return {"namespace": namespace, "keys": list(ns_map.keys()), "count": len(ns_map)} + + def delete(self, key: str, namespace: str = "default") -> Dict[str, Any]: + with self._lock: + ns_map = self._data.get(namespace, {}) + if key not in ns_map: + return {"error": "not_found"} + prev_rev = self._revisions.get((namespace, key), 0) + rev = prev_rev + 1 + del ns_map[key] + self._revisions[(namespace, key)] = rev + self._append_journal({"op": 0, "ns": namespace, "k": key, "rev": rev}) + return {"ok": True} + + def dump_namespace(self, namespace: str = "default", limit: Optional[int] = None, include_metadata: bool = False) -> Dict[str, Any]: + with self._lock: + ns_map = self._data.get(namespace, {}) + items = [] + for i, (k, rec) in enumerate(ns_map.items()): + if limit is not None and i >= limit: + break + entry = {"key": k, "value": rec["v"], "timestamp": rec["t"], "rev": rec.get("rev", 0)} + if include_metadata and "m" in rec: + entry["metadata"] = rec["m"] + items.append(entry) + return {"namespace": namespace, "items": items, "count": len(ns_map)} + + def stats(self) -> Dict[str, Any]: + with self._lock: + namespaces = {ns: len(kvs) for ns, kvs in self._data.items()} + total_keys = sum(namespaces.values()) + snapshot_bytes = os.path.getsize(self.snapshot_path) if os.path.exists(self.snapshot_path) else 0 + journal_bytes = os.path.getsize(self.journal_path) if os.path.exists(self.journal_path) else 0 + return { + "namespaces": namespaces, + "total_keys": total_keys, + "snapshot_bytes": snapshot_bytes, + "journal_bytes": journal_bytes, + "last_compaction": self._last_compaction, + "corrupt_entries": self._corrupt_entries, + } diff --git a/src/mcp/datastore-mcp/uv.lock 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macronutrients (calories, protein, carbs, fat) from natural language food descriptions or recipe-style ingredient lists. + +## Features + +- Natural language parsing (e.g. "2 eggs and 2 slices wholemeal toast with butter") +- Recipe list parsing (array of ingredients) +- Multiple data providers with graceful fallback: + - Nutritionix (preferred, natural-language endpoint) + - Edamam Nutrition API + - OpenFoodFacts (simple product search; per-100g basis) +- MCP Tools, Resource and Prompt + +## Setup + +```bash +cd src/mcp/macros-mcp +uv sync +``` + +### .env configuration (recommended) + +Create a `.env` file in this folder with any available API keys: + +``` +NUTRITIONIX_APP_ID= +NUTRITIONIX_API_KEY= +EDAMAM_APP_ID= +EDAMAM_APP_KEY= +``` + +These are loaded automatically at startup via `python-dotenv`. + +### Environment variables + +Create a `.env` or otherwise set variables for best accuracy: + +- Nutritionix (recommended): + - `NUTRITIONIX_APP_ID` + - `NUTRITIONIX_API_KEY` +- Edamam (optional fallback): + - `EDAMAM_APP_ID` + - `EDAMAM_APP_KEY` + +If no keys are set, the server falls back to OpenFoodFacts when possible (lower confidence). + +### Run the server + +- Dev mode UI: + +```bash +uv run mcp dev main.py +``` + +- For agent consumption (HTTP on port 8010): + +```bash +uv run main.py +``` + +## Tools + +```python +analyze_macros(description: str) -> Dict +analyze_recipe(ingredients: List[str]) -> Dict +``` + +## Resource + +- `macros://{query}` → formatted text summary + +## Prompt + +```python +macros_summary_prompt(description: str, target_calories: Optional[int] = None) +``` + +## Example + +```python +analyze_macros("chicken breast 200g with rice 150g and olive oil 1 tbsp") + +analyze_recipe([ + "2 eggs", + "2 slices wholemeal bread", + "1 tbsp butter" +]) +``` + +## Notes + +- Estimates depend on provider data and serving parsing. Treat outputs as approximate. +- Nutritionix/Edamam may apply internal databases and heuristics; OpenFoodFacts results are per 100g and may need scaling. diff --git a/src/mcp/macros-mcp/main.py b/src/mcp/macros-mcp/main.py new file mode 100644 index 0000000..fe9db0b --- /dev/null +++ b/src/mcp/macros-mcp/main.py @@ -0,0 +1,74 @@ +""" +Macros MCP Server - Estimate macronutrients from a natural language food description. + +Integration strategy: +- Nutritionix Natural Language API (preferred when NUTRITIONIX_APP_ID/KEY set) +- Edamam Nutrition Data API (when EDAMAM_APP_ID/KEY set) +- OpenFoodFacts search as a fallback for single-product queries without keys +""" + +from typing import Any, Dict, List, Optional + +from mcp.server.fastmcp import FastMCP +from dotenv import load_dotenv + +from nutrition_service import ( + estimate_macros_from_description, + estimate_macros_from_ingredients, + build_formatted_resource, +) + + +load_dotenv() + +mcp = FastMCP("Macros", port=8010) + + +@mcp.tool() +def analyze_macros(description: str) -> Dict[str, Any]: + """Estimate macronutrients for a natural language food description. + + Args: + description: Natural language description, e.g. "2 eggs and 2 slices of toast with butter" + + Returns: + Dict containing total macros, per-item breakdown, serving guidance, source and confidence + """ + return estimate_macros_from_description(description) + + +@mcp.tool() +def analyze_recipe(ingredients: List[str]) -> Dict[str, Any]: + """Estimate macronutrients for a list of ingredient lines (recipe style). + + Args: + ingredients: List of ingredient lines, e.g. ["2 eggs", "2 slices wholemeal bread", "1 tbsp butter"] + + Returns: + Dict containing total macros and per-ingredient breakdown + """ + return estimate_macros_from_ingredients(ingredients) + + +@mcp.resource("macros://{query}") +def get_macros_resource(query: str) -> str: + """Formatted macros summary as a resource.""" + data = estimate_macros_from_description(query) + return build_formatted_resource(query, data) + + +@mcp.prompt() +def macros_summary_prompt(description: str, target_calories: Optional[int] = None) -> str: + """Generate a prompt to summarize macros and portion suggestions.""" + base = f"Analyze the following food and estimate macronutrients: {description}." + if target_calories: + return base + f" Then suggest a portion to target approximately {target_calories} kcal." + return base + +@mcp.prompt() +def macros_for_a_dish_prompt(dish_name: str) -> str: + """Generate a prompt to describe the macronutrient profile of a dish.""" + return f"Describe the macronutrient profile of {dish_name}." + +if __name__ == "__main__": + mcp.run("streamable-http") diff --git a/src/mcp/macros-mcp/nutrition_service.py b/src/mcp/macros-mcp/nutrition_service.py new file mode 100644 index 0000000..057d748 --- /dev/null +++ b/src/mcp/macros-mcp/nutrition_service.py @@ -0,0 +1,205 @@ +from __future__ import annotations + +import os +from typing import Any, Dict, List, Optional, Tuple + +import requests + +from utils import clamp_non_negative, round2 + + +class NutritionSource: + NUTRITIONIX = "nutritionix" + EDAMAM = "edamam" + OPENFOODFACTS = "openfoodfacts" + + +def _nutritionix_available() -> bool: + return bool(os.getenv("NUTRITIONIX_APP_ID") and os.getenv("NUTRITIONIX_API_KEY")) + + +def _edamam_available() -> bool: + return bool(os.getenv("EDAMAM_APP_ID") and os.getenv("EDAMAM_APP_KEY")) + + +def _call_nutritionix_nlp(query: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]: + url = "https://trackapi.nutritionix.com/v2/natural/nutrients" + headers = { + "x-app-id": os.getenv("NUTRITIONIX_APP_ID", ""), + "x-app-key": os.getenv("NUTRITIONIX_API_KEY", ""), + "Content-Type": "application/json", + } + try: + resp = requests.post(url, json={"query": query}, headers=headers, timeout=15) + if resp.status_code == 200: + return resp.json(), None + return None, f"nutritionix_error:{resp.status_code}" + except Exception as ex: # noqa: BLE001 + return None, f"nutritionix_exception:{ex}" + + +def _call_edamam_nlp(query: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]: + # Edamam Nutrition Analysis v2 - natural language endpoint + # https://developer.edamam.com/edamam-nutrition-api + app_id = os.getenv("EDAMAM_APP_ID", "") + app_key = os.getenv("EDAMAM_APP_KEY", "") + url = f"https://api.edamam.com/api/nutrition-data?app_id={app_id}&app_key={app_key}&nutrition-type=logging&ingr={requests.utils.quote(query)}" + try: + resp = requests.get(url, timeout=15) + if resp.status_code == 200: + return resp.json(), None + return None, f"edamam_error:{resp.status_code}" + except Exception as ex: # noqa: BLE001 + return None, f"edamam_exception:{ex}" + + +def _call_openfoodfacts_search(query: str) -> Tuple[Optional[Dict[str, Any]], Optional[str]]: + # Simple search - best effort, good for branded items + url = "https://world.openfoodfacts.org/cgi/search.pl" + params = {"search_terms": query, "search_simple": 1, "json": 1, "page_size": 1} + try: + resp = requests.get(url, params=params, timeout=15) + if resp.status_code == 200: + return resp.json(), None + return None, f"openfoodfacts_error:{resp.status_code}" + except Exception as ex: # noqa: BLE001 + return None, f"openfoodfacts_exception:{ex}" + + +def _normalize_nutritionix(payload: Dict[str, Any]) -> Dict[str, Any]: + foods = payload.get("foods", []) + items: List[Dict[str, Any]] = [] + total = {"calories": 0.0, "protein_g": 0.0, "carbs_g": 0.0, "fat_g": 0.0} + for f in foods: + item = { + "name": f.get("food_name"), + "serving_qty": f.get("serving_qty"), + "serving_unit": f.get("serving_unit"), + "calories": round2(f.get("nf_calories", 0.0)), + "protein_g": round2(f.get("nf_protein", 0.0)), + "carbs_g": round2(f.get("nf_total_carbohydrate", 0.0)), + "fat_g": round2(f.get("nf_total_fat", 0.0)), + } + items.append(item) + total["calories"] += item["calories"] + total["protein_g"] += item["protein_g"] + total["carbs_g"] += item["carbs_g"] + total["fat_g"] += item["fat_g"] + + for k in total: + total[k] = round2(total[k]) + + return { + "source": NutritionSource.NUTRITIONIX, + "confidence": 0.9 if items else 0.3, + "items": items, + "total": total, + } + + +def _normalize_edamam(payload: Dict[str, Any], query: str) -> Dict[str, Any]: + # Edamam nutrition-data returns totals for the full text + total = { + "calories": round2(payload.get("calories", 0.0)), + "protein_g": round2(payload.get("totalNutrients", {}).get("PROCNT", {}).get("quantity", 0.0)), + "carbs_g": round2(payload.get("totalNutrients", {}).get("CHOCDF", {}).get("quantity", 0.0)), + "fat_g": round2(payload.get("totalNutrients", {}).get("FAT", {}).get("quantity", 0.0)), + } + return { + "source": NutritionSource.EDAMAM, + "confidence": 0.7, + "items": [{"name": query, **total}], + "total": total, + } + + +def _normalize_openfoodfacts(payload: Dict[str, Any]) -> Dict[str, Any]: + products = payload.get("products", []) + if not products: + return {"source": NutritionSource.OPENFOODFACTS, "confidence": 0.2, "items": [], "total": {}} + p = products[0] + nutr = p.get("nutriments", {}) + per100kcal = { + "calories": round2(nutr.get("energy-kcal_100g", 0.0)), + "protein_g": round2(nutr.get("proteins_100g", 0.0)), + "carbs_g": round2(nutr.get("carbohydrates_100g", 0.0)), + "fat_g": round2(nutr.get("fat_100g", 0.0)), + } + return { + "source": NutritionSource.OPENFOODFACTS, + "confidence": 0.5, + "items": [{"name": p.get("product_name", "product"), **per100kcal, "basis": "per_100g"}], + "total": per100kcal, + } + + +def estimate_macros_from_description(description: str) -> Dict[str, Any]: + description = description.strip() + tried: List[str] = [] + + if _nutritionix_available(): + data, err = _call_nutritionix_nlp(description) + tried.append(NutritionSource.NUTRITIONIX) + if data: + return _normalize_nutritionix(data) + + if _edamam_available(): + data, err = _call_edamam_nlp(description) + tried.append(NutritionSource.EDAMAM) + if data: + return _normalize_edamam(data, description) + + data, err = _call_openfoodfacts_search(description) + tried.append(NutritionSource.OPENFOODFACTS) + if data: + return _normalize_openfoodfacts(data) + + return { + "source": "none", + "confidence": 0.0, + "items": [], + "total": {}, + "error": "No data sources available or reachable", + "tried": tried, + } + + +def estimate_macros_from_ingredients(ingredients: List[str]) -> Dict[str, Any]: + if not ingredients: + return {"source": "none", "confidence": 0.0, "items": [], "total": {}} + combined = ", ".join(ingredients) + return estimate_macros_from_description(combined) + + +def build_formatted_resource(query: str, data: Dict[str, Any]) -> str: + lines: List[str] = [] + lines.append(f"Food: {query}") + lines.append(f"Source: {data.get('source')} | Confidence: {data.get('confidence')}") + + total = data.get("total", {}) + if total: + lines.append("Total (approx):") + lines.append( + f" Calories: {total.get('calories', 0)} kcal | Protein: {total.get('protein_g', 0)} g | Carbs: {total.get('carbs_g', 0)} g | Fat: {total.get('fat_g', 0)} g" + ) + items = data.get("items", []) + if items: + lines.append("Items:") + for it in items: + name = it.get("name", "item") + extra = [] + if it.get("serving_qty"): + extra.append(f"{it.get('serving_qty')} {it.get('serving_unit')}") + if it.get("basis"): + extra.append(f"basis: {it.get('basis')}") + suffix = f" ({', '.join(extra)})" if extra else "" + lines.append( + f" - {name}{suffix}: {it.get('calories', 0)} kcal, P {it.get('protein_g', 0)} g, C {it.get('carbs_g', 0)} g, F {it.get('fat_g', 0)} g" + ) + if data.get("error"): + lines.append(f"Error: {data.get('error')}") + if data.get("tried"): + lines.append(f"Tried sources: {', '.join(data['tried'])}") + return "\n".join(lines) + + diff --git a/src/mcp/macros-mcp/pyproject.toml b/src/mcp/macros-mcp/pyproject.toml new file mode 100644 index 0000000..9aefe6e --- /dev/null +++ b/src/mcp/macros-mcp/pyproject.toml @@ -0,0 +1,11 @@ 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--git a/src/mcp/nutritional-plan-mcp/README.md b/src/mcp/nutritional-plan-mcp/README.md new file mode 100644 index 0000000..8a50b81 --- /dev/null +++ b/src/mcp/nutritional-plan-mcp/README.md @@ -0,0 +1,71 @@ +# Nutritional Plan MCP Server + +Compose long-term daily calorie and macro targets based on a user profile and goal. + +## Features + +- Calculates BMR (Mifflin–St Jeor) and TDEE (activity multipliers) +- Goal-driven targets: lose weight, maintain, gain muscle +- Adjustable weekly change rate (kg/week) +- Macro allocation heuristics with protein emphasis +- Tools, Resource and Prompt available + +## Setup + +```bash +cd src/mcp/nutritional-plan-mcp +uv sync +``` + +## Run + +- Dev UI: + +```bash +uv run mcp dev main.py +``` + +- For agent (HTTP on port 8011): + +```bash +uv run main.py +``` + +## Tools + +```python +compose_plan( + sex: str, + age_years: int, + height_cm: float, + weight_kg: float, + activity_level: str = "sedentary", + goal: str = "lose_weight", + weekly_rate: Optional[float] = None, +) -> Dict +``` + +## Resource + +- `nutrition-plan://{sex}/{age}/{height}/{weight}/{activity}/{goal}` → formatted plan + +## Prompt + +```python +plan_summary_prompt(goal: str, constraints: Optional[str] = None) +``` + +## Example + +```python +# Example: male, 30y, 170cm, 68kg, lightly active, goal: lose weight ~0.4 kg/week +compose_plan( + sex="male", age_years=30, height_cm=170, weight_kg=68, + activity_level="lightly_active", goal="lose_weight", weekly_rate=0.4 +) +``` + +## Notes + +- Defaults avoid extreme deficits/surpluses by clamping around BMR. +- Reassess every 2–4 weeks and adjust targets based on progress. diff --git a/src/mcp/nutritional-plan-mcp/main.py b/src/mcp/nutritional-plan-mcp/main.py new file mode 100644 index 0000000..8367523 --- /dev/null +++ b/src/mcp/nutritional-plan-mcp/main.py @@ -0,0 +1,129 @@ +""" +Nutritional Plan MCP - Compose long-term daily calorie and macro targets based on user profile and goal. +""" + +from typing import Dict, Any, Optional + +from mcp.server.fastmcp import FastMCP + +from plan_service import UserProfile, plan_calories, build_text_plan + + +mcp = FastMCP("NutritionalPlan", port=8011) + + +@mcp.tool() +def compose_plan( + sex: str, + age_years: int, + height_cm: float, + weight_kg: float, + activity_level: str = "sedentary", + goal: str = "lose_weight", + weekly_rate: Optional[float] = None, +) -> Dict[str, Any]: + """Compose a nutrition plan with daily calories and macros. + + Args: + sex: "male" or "female" + age_years: Age in years + height_cm: Height in centimeters + weight_kg: Weight in kilograms + activity_level: sedentary|lightly_active|moderately_active|very_active|extra_active + goal: lose_weight|maintain|gain_muscle + weekly_rate: Optional kg/week rate (e.g., 0.4 for loss, 0.25 for gain) + """ + profile = UserProfile( + sex=sex, + age_years=age_years, + height_cm=height_cm, + weight_kg=weight_kg, + activity_level=activity_level, + goal=goal, + weekly_rate=weekly_rate, + ) + return plan_calories(profile) + + +@mcp.resource("nutrition-plan://{sex}/{age}/{height}/{weight}/{activity}/{goal}") +def plan_resource( + sex: str, + age: int, + height: float, + weight: float, + activity: str, + goal: str, +) -> str: + profile = UserProfile( + sex=sex, + age_years=age, + height_cm=height, + weight_kg=weight, + activity_level=activity, + goal=goal, + ) + data = plan_calories(profile) + return build_text_plan(profile, data) + + +@mcp.prompt() +def plan_summary_prompt(goal: str, constraints: Optional[str] = None) -> str: + base = f"Create a concise nutrition plan summary to {goal}." + if constraints: + base += f" Consider: {constraints}." + base += " Include daily calories, macro split, and 3 guidance bullets." + return base + +@mcp.prompt() +def meal_plan_for_rest_of_day_prompt( + current_time: str, + meals_eaten: str, + daily_calories: int, + protein_g: int, + carbs_g: int, + fats_g: int, +) -> str: + return ( + f"It's {current_time} and I've eaten {meals_eaten} today. " + f"My daily targets are {daily_calories} kcal, {protein_g}g protein, " + f"{carbs_g}g carbs, and {fats_g}g fats. Suggest meals for the rest of the day " + "to meet these targets." + ) + +@mcp.prompt() +def what_can_I_eat_today_prompt( + already_eaten: str, +) -> str: + return ( + f"I've already eaten {already_eaten} today. " + "Based on a balanced diet, what else can I eat today?" + ) + +@mcp.prompt() +def what_is_my_nutrition_plan_for_prompt( + WhichDay: Optional[str] = None, +) -> str: + base = "Get my nutrition plan from storage." + if WhichDay: + base += f" Focus on {WhichDay}." + + base += " Check the memory store and retrieve it from there. If there isn't any plan, say I don't have a plan yet. And offer to create one for me." + return base + + +@mcp.prompt() +def retrieve_my_nutrition_plan_prompt( + WhichDay: Optional[str] = None, +) -> str: + return "Check the memory store and retrieve my nutrition plan." + ( + f" Focus on {WhichDay}." if WhichDay else "" + ) + +@mcp.prompt() +def update_my_nutrition_plan_prompt( + update_details: str, +) -> str: + return f"Update my nutrition plan in the memory store with these details: {update_details}." + +if __name__ == "__main__": + mcp.run("streamable-http") diff --git a/src/mcp/nutritional-plan-mcp/plan_service.py b/src/mcp/nutritional-plan-mcp/plan_service.py new file mode 100644 index 0000000..1a4c9f1 --- /dev/null +++ b/src/mcp/nutritional-plan-mcp/plan_service.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, Optional + + +@dataclass +class UserProfile: + sex: str # "male" | "female" + age_years: int + height_cm: float + weight_kg: float + activity_level: str # sedentary, lightly_active, moderately_active, very_active, extra_active + goal: str # lose_weight, maintain, gain_muscle + weekly_rate: Optional[float] = None # kg per week (positive for gain, positive for loss target) + + +def calculate_bmr_mifflin(profile: UserProfile) -> float: + # Mifflin-St Jeor + s = 5 if profile.sex.lower() == "male" else -161 + return 10 * profile.weight_kg + 6.25 * profile.height_cm - 5 * profile.age_years + s + + +def activity_multiplier(level: str) -> float: + mapping = { + "sedentary": 1.2, + "lightly_active": 1.375, + "moderately_active": 1.55, + "very_active": 1.725, + "extra_active": 1.9, + } + return mapping.get(level.lower(), 1.2) + + +def protein_target_grams(profile: UserProfile) -> float: + # Simple heuristic: vary by goal/activity + if profile.goal == "gain_muscle": + grams_per_kg = 1.8 + elif profile.goal == "lose_weight": + grams_per_kg = 1.6 + else: + grams_per_kg = 1.4 + return round(profile.weight_kg * grams_per_kg, 1) + + +def fat_target_grams(total_calories: int, profile: UserProfile) -> float: + # 25-35% calories from fat depending on goal + if profile.goal == "gain_muscle": + pct = 0.25 + elif profile.goal == "lose_weight": + pct = 0.30 + else: + pct = 0.30 + return round((total_calories * pct) / 9.0, 1) + + +def carbs_from_remaining(total_calories: int, protein_g: float, fat_g: float) -> float: + calories_from_protein = protein_g * 4 + calories_from_fat = fat_g * 9 + remaining = max(0.0, total_calories - calories_from_protein - calories_from_fat) + return round(remaining / 4.0, 1) + + +def plan_calories(profile: UserProfile) -> Dict: + bmr = calculate_bmr_mifflin(profile) + tdee = bmr * activity_multiplier(profile.activity_level) + + # energy balance: 7700 kcal ~ 1 kg + target_delta = 0.0 + if profile.goal == "lose_weight": + weekly = profile.weekly_rate or 0.4 # kg/week default + target_delta = - (weekly * 7700) / 7.0 + elif profile.goal == "gain_muscle": + weekly = profile.weekly_rate or 0.25 + target_delta = (weekly * 7700) / 7.0 + + target_cals = int(round(tdee + target_delta)) + min_safe = int(round(bmr * 1.1)) # avoid too low + target_cals = max(target_cals, min_safe) + + protein_g = protein_target_grams(profile) + fat_g = fat_target_grams(target_cals, profile) + carbs_g = carbs_from_remaining(target_cals, protein_g, fat_g) + + return { + "bmr": int(round(bmr)), + "tdee": int(round(tdee)), + "target_calories": target_cals, + "macros": { + "protein_g": protein_g, + "carbs_g": carbs_g, + "fat_g": fat_g, + }, + "assumptions": { + "formula": "Mifflin-St Jeor", + "weekly_rate_kg": profile.weekly_rate or (0.4 if profile.goal == "lose_weight" else 0.25 if profile.goal == "gain_muscle" else 0.0), + "activity_multiplier": activity_multiplier(profile.activity_level), + "kcal_per_kg": 7700, + } + } + + +def build_text_plan(profile: UserProfile, data: Dict) -> str: + lines = [] + lines.append("Nutrition Plan") + lines.append(f"Sex: {profile.sex}, Age: {profile.age_years}, Height: {profile.height_cm} cm, Weight: {profile.weight_kg} kg") + lines.append(f"Activity: {profile.activity_level}, Goal: {profile.goal}") + lines.append("") + lines.append(f"BMR: {data['bmr']} kcal | TDEE: {data['tdee']} kcal") + lines.append(f"Daily target: {data['target_calories']} kcal") + m = data["macros"] + lines.append(f"Macros (approx): Protein {m['protein_g']} g, Carbs {m['carbs_g']} g, Fat {m['fat_g']} g") + lines.append("") + lines.append("Assumptions:") + a = data["assumptions"] + lines.append(f" - Formula: {a['formula']}") + lines.append(f" - Weekly rate: {a['weekly_rate_kg']} kg/week") + lines.append(f" - Activity multiplier: {a['activity_multiplier']}") + lines.append(f" - Energy per kg: {a['kcal_per_kg']} kcal") + lines.append("") + lines.append("Guidance:") + lines.append(" - Spread protein across 3–4 meals.") + lines.append(" - Emphasize whole foods, fiber, and hydration.") + lines.append(" - Reassess every 2–4 weeks and adjust based on progress.") + return 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b/src/mcp/recipemaker-mcp/generator.py new file mode 100644 index 0000000..96085ec --- /dev/null +++ b/src/mcp/recipemaker-mcp/generator.py @@ -0,0 +1,54 @@ +def generate_recipes(ingredients): + """ + Generate suitable recipes based on a list of ingredients. + Returns a list of recipe strings. + """ + if not ingredients: + return ["No ingredients provided."] + recipes = [] + + # Expanded mock recipes + if "egg" in ingredients and "bread" in ingredients: + recipes.append("French Toast: Dip bread in beaten eggs, fry until golden.") + if "egg" in ingredients and "cheese" in ingredients: + recipes.append("Cheese Omelette: Beat eggs, add cheese, cook in pan.") + if "tomato" in ingredients and "pasta" in ingredients: + recipes.append("Tomato Pasta: Cook pasta, add tomato sauce, simmer.") + if "tomato" in ingredients and "cheese" in ingredients and "bread" in ingredients: + recipes.append("Cheese & Tomato Sandwich: Layer cheese and tomato between bread slices, grill.") + if "rice" in ingredients and "chicken" in ingredients: + recipes.append("Chicken Fried Rice: Stir-fry chicken and rice with veggies.") + if "rice" in ingredients and "egg" in ingredients: + recipes.append("Egg Fried Rice: Stir-fry rice with scrambled eggs and vegetables.") + if "potato" in ingredients and "cheese" in ingredients: + recipes.append("Cheesy Mashed Potatoes: Boil potatoes, mash, mix with cheese.") + if "potato" in ingredients and "egg" in ingredients: + recipes.append("Potato Frittata: Slice potatoes, mix with eggs, bake or fry.") + if "chicken" in ingredients and "tomato" in ingredients: + recipes.append("Chicken Tomato Stew: Cook chicken with tomatoes and spices.") + if "pasta" in ingredients and "cheese" in ingredients: + recipes.append("Cheesy Pasta: Cook pasta, mix with melted cheese.") + if "bread" in ingredients and "butter" in ingredients: + recipes.append("Buttered Toast: Toast bread, spread with butter.") + if "apple" in ingredients and "cinnamon" in ingredients: + recipes.append("Cinnamon Apple Slices: Slice apples, sprinkle with cinnamon, bake.") + if "banana" in ingredients and "milk" in ingredients: + recipes.append("Banana Smoothie: Blend banana with milk.") + if "carrot" in ingredients and "potato" in ingredients: + recipes.append("Carrot & Potato Soup: Boil carrots and potatoes, blend, season.") + if "lettuce" in ingredients and "tomato" in ingredients: + recipes.append("Simple Salad: Toss lettuce and tomato with dressing.") + if "egg" in ingredients and "milk" in ingredients: + recipes.append("Scrambled Eggs: Beat eggs with milk, cook in pan.") + if "chicken" in ingredients and "rice" in ingredients and "peas" in ingredients: + recipes.append("Chicken & Pea Rice Bowl: Cook chicken, rice, and peas together.") + if "pasta" in ingredients and "spinach" in ingredients: + recipes.append("Spinach Pasta: Cook pasta, toss with sautéed spinach.") + if "cheese" in ingredients and "tomato" in ingredients and "pasta" in ingredients: + recipes.append("Three Cheese Tomato Pasta: Cook pasta, add tomato sauce and three cheeses.") + + # Add more mock variants as needed + + if not recipes: + recipes.append(f"No matching recipes for: {', '.join(ingredients)}") + return recipes diff --git a/src/mcp/recipemaker-mcp/main.py b/src/mcp/recipemaker-mcp/main.py new file mode 100644 index 0000000..d750870 --- /dev/null +++ b/src/mcp/recipemaker-mcp/main.py @@ -0,0 +1,39 @@ +from mcp.server.fastmcp import FastMCP +from generator import generate_recipes + +# Create an MCP server +# you can add the port here so that it doesnt clash with other mcp servers +mcp = FastMCP("myname", port=8123) + +# Add an addition tool + +# Recipe generator tool +@mcp.tool() +def recipe_from_ingredients(ingredients: list[str]) -> list[str]: + """Generate suitable recipes from a list of ingredients""" + return generate_recipes(ingredients) + + +# Add a dynamic greeting resource +@mcp.resource("greeting://{name}") +def get_greeting(name: str) -> str: + """Get a personalized greeting""" + return f"Hello, {name}!" + +# Add a recipe prompt +@mcp.prompt() +def recipe_suggestion_prompt(ingredients: list[str]) -> str: + """Generate a prompt for recipe suggestions""" + if not ingredients: + return "Please provide some ingredients to get recipe suggestions." + return f"Suggest recipes using the following ingredients: {', '.join(ingredients)}" + +# Add a recipe resource +@mcp.resource("recipe://{ingredients}") +def get_recipe_resource(ingredients: list[str]) -> str: + """Get recipes as a formatted resource""" + recipes = generate_recipes(ingredients) + return "\n".join(recipes) + +if __name__ == "__main__": + mcp.run("streamable-http") \ No newline at end of file diff --git a/src/mcp/recipemaker-mcp/pyproject.toml b/src/mcp/recipemaker-mcp/pyproject.toml new file mode 100644 index 0000000..b1bde6b --- /dev/null +++ b/src/mcp/recipemaker-mcp/pyproject.toml @@ -0,0 +1,9 @@ +[project] +name = "recipemaker-mcp" +version = "0.1.0" +description = 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= 67976, upload-time = "2025-09-23T13:33:45.842Z" }, +] diff --git a/src/mcp/restaurants-mcp/.python-version b/src/mcp/restaurants-mcp/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/src/mcp/restaurants-mcp/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/src/mcp/restaurants-mcp/README.md b/src/mcp/restaurants-mcp/README.md new file mode 100644 index 0000000..1607ee3 --- /dev/null +++ b/src/mcp/restaurants-mcp/README.md @@ -0,0 +1,140 @@ +# Restaurants MCP Server + +A comprehensive Model Context Protocol (MCP) server for restaurant discovery, menu browsing, and reservation booking. This service provides detailed restaurant information with nutritional data for restaurants in London, Paris, and Rome, designed to integrate with nutrition and macro tracking services. + +## Features + +### 🍽️ Restaurant Discovery +- Browse restaurants by location (London, Paris, Rome) +- Search by cuisine type, price range, and rating +- Detailed restaurant information including hours, contact details, and special features + +### 📋 Menu & Nutrition +- Complete menu items with detailed nutritional information +- Ingredient-level nutritional breakdown (calories, protein, carbs, fat, fiber) +- Allergen and dietary restriction information +- Search and filter menu items by nutritional criteria + +### 📅 Reservation System +- Book restaurant reservations with availability checking +- Manage reservations (view, cancel) +- Support for special requests and dietary accommodations + +### 🔗 Integration Ready +- Designed to integrate with macros-mcp for nutritional analysis +- Compatible with nutritional-plan-mcp for meal planning +- Works with trivia-mcp for nutrition education + +## Installation + +```bash +# Install dependencies +uv sync + +# Run the server +uv run mcp dev main.py +``` + +## Available Tools + +### Restaurant Discovery +- `get_restaurants(location)` - Get restaurants by city +- `search_restaurants_tool(location, cuisine_type, price_range, rating_min)` - Search with filters +- `get_restaurant_details(restaurant_id)` - Get detailed restaurant information + +### Menu & Nutrition +- `get_restaurant_menu_tool(restaurant_id)` - Get complete menu with nutritional data +- `search_menu_items_tool(restaurant_id, category, max_calories, dietary_tags, allergen_free)` - Search menu items + +### Reservations +- `book_restaurant_reservation(restaurant_id, customer_name, email, phone, date, time, party_size, special_requests)` - Book a reservation +- `get_reservation_details(reservation_id)` - Get reservation information +- `cancel_restaurant_reservation(reservation_id)` - Cancel a reservation +- `get_restaurant_availability_tool(restaurant_id, date)` - Check available time slots + +## Available Resources + +- `restaurants://{restaurant_id}` - Formatted restaurant information +- `restaurants://menu/{restaurant_id}` - Formatted menu with nutritional data + +## Available Prompts + +- `restaurant_booking_prompt(location, cuisine_type)` - Restaurant booking assistance +- `nutrition_restaurant_prompt(restaurant_id, dietary_goals)` - Nutrition-focused guidance + +## Sample Data + +The service includes hardcoded data for restaurants in London, Paris, and Rome: + +### London Restaurants +- **The Ivy** - Classic British cuisine ($$$$, 4.5★) +- **Dishoom** - Bombay-style Indian street food ($$, 4.3★) + +### Paris Restaurants +- **L'Atelier de Joël Robuchon** - Michelin-starred French cuisine ($$$$, 4.7★) +- **Le Comptoir du Relais** - Traditional French bistro ($$$, 4.2★) + +### Rome Restaurants +- **Roscioli** - Historic Roman trattoria with traditional cuisine ($$$, 4.6★) +- **Armando al Pantheon** - Family-run trattoria near the Pantheon ($$, 4.4★) + +## Nutritional Data Structure + +Each menu item includes: +- **Macronutrients**: Calories, protein, carbohydrates, fat, fiber +- **Ingredients**: Detailed breakdown with nutritional values per ingredient +- **Allergens**: Comprehensive allergen information +- **Dietary Tags**: Vegetarian, vegan, gluten-free, etc. +- **Preparation Info**: Cooking time and spice level + +## Integration Examples + +### With Macros MCP +```python +# Get menu item nutritional data +menu_item = get_restaurant_menu_tool("london_1") +# Use macros-mcp to analyze nutritional content +macros_analysis = analyze_macros(menu_item["description"]) +``` + +### With Nutritional Plan MCP +```python +# Search for low-calorie options +low_calorie_items = search_menu_items_tool( + restaurant_id="paris_1", + max_calories=500, + dietary_tags=["vegetarian"] +) +# Integrate with meal planning +``` + +## Configuration + +The server runs on port 8014 by default. To change the port, modify the `FastMCP` initialization in `main.py`. + +## Development + +### Project Structure +``` +restaurants-mcp/ +├── main.py # MCP server implementation +├── models.py # Data models and structures +├── restaurant_data.py # Hardcoded restaurant data +├── restaurant_service.py # Business logic and operations +├── pyproject.toml # Dependencies and configuration +└── README.md # This file +``` + +### Adding New Restaurants +1. Add restaurant data to `restaurant_data.py` +2. Include detailed menu items with nutritional information +3. Update the data models if needed + +### Extending Functionality +- Add new tools in `main.py` +- Implement business logic in `restaurant_service.py` +- Update data models in `models.py` as needed + +## License + +This project is part of the Hackathon 2025 MCP services ecosystem. \ No newline at end of file diff --git a/src/mcp/restaurants-mcp/main.py b/src/mcp/restaurants-mcp/main.py new file mode 100644 index 0000000..7c5e6c0 --- /dev/null +++ b/src/mcp/restaurants-mcp/main.py @@ -0,0 +1,424 @@ +""" +Restaurants MCP Server - Restaurant discovery, menu browsing, and reservation booking. + +To run this server: + uv run mcp dev main.py + +Provides restaurant information, menu details with nutritional data, and booking capabilities +for restaurants in London, Paris, and Rome. +""" + +from typing import Dict, Any, Optional, List +from mcp.server.fastmcp import FastMCP + +from restaurant_service import ( + get_restaurants_by_location, + get_restaurant_by_id, + search_restaurants, + get_restaurant_menu, + search_menu_items, + book_reservation, + get_reservation, + cancel_reservation, + get_restaurant_availability +) + +mcp = FastMCP("Restaurants", port=8014) + +# Restaurant discovery tools +@mcp.tool() +def get_restaurants(location: Optional[str] = None) -> Dict[str, Any]: + """Get restaurants by location (London, Paris, or Rome) + + Args: + location: City name - "London", "Paris", "Rome", or None for all + + Returns: + List of restaurants with basic information + """ + restaurants = get_restaurants_by_location(location) if location else get_restaurants_by_location("all") + + return { + "restaurants": [ + { + "id": r.id, + "name": r.name, + "description": r.description, + "cuisine_type": r.cuisine_type, + "location": r.location, + "address": r.address, + "phone": r.phone, + "rating": r.rating, + "price_range": r.price_range, + "special_features": r.special_features + } + for r in restaurants + ], + "total_count": len(restaurants) + } + +@mcp.tool() +def search_restaurants_tool( + location: Optional[str] = None, + cuisine_type: Optional[str] = None, + price_range: Optional[str] = None, + rating_min: Optional[float] = None +) -> Dict[str, Any]: + """Search restaurants with filters + + Args: + location: City name (London, Paris, Rome) + cuisine_type: Type of cuisine (British, French, Indian, Italian, etc.) + price_range: Price range ($, $$, $$$, $$$$) + rating_min: Minimum rating (1.0-5.0) + + Returns: + Filtered list of restaurants + """ + restaurants = search_restaurants(location, cuisine_type, price_range, rating_min) + + return { + "restaurants": [ + { + "id": r.id, + "name": r.name, + "description": r.description, + "cuisine_type": r.cuisine_type, + "location": r.location, + "rating": r.rating, + "price_range": r.price_range, + "special_features": r.special_features + } + for r in restaurants + ], + "total_count": len(restaurants), + "filters_applied": { + "location": location, + "cuisine_type": cuisine_type, + "price_range": price_range, + "rating_min": rating_min + } + } + +@mcp.tool() +def get_restaurant_details(restaurant_id: str) -> Dict[str, Any]: + """Get detailed information about a specific restaurant + + Args: + restaurant_id: Unique ID of the restaurant + + Returns: + Detailed restaurant information including menu and booking details + """ + restaurant = get_restaurant_by_id(restaurant_id) + if not restaurant: + return {"error": f"Restaurant with ID {restaurant_id} not found"} + + return { + "id": restaurant.id, + "name": restaurant.name, + "description": restaurant.description, + "cuisine_type": restaurant.cuisine_type, + "location": restaurant.location, + "address": restaurant.address, + "phone": restaurant.phone, + "email": restaurant.email, + "rating": restaurant.rating, + "price_range": restaurant.price_range, + "opening_hours": restaurant.opening_hours, + "capacity": restaurant.capacity, + "booking_advance_days": restaurant.booking_advance_days, + "special_features": restaurant.special_features, + "coordinates": restaurant.coordinates, + "menu_item_count": len(restaurant.menu) + } + +# Menu and nutrition tools +@mcp.tool() +def get_restaurant_menu_tool(restaurant_id: str) -> Dict[str, Any]: + """Get menu for a specific restaurant + + Args: + restaurant_id: Unique ID of the restaurant + + Returns: + Complete menu with nutritional information + """ + menu = get_restaurant_menu(restaurant_id) + if not menu: + return {"error": f"Restaurant with ID {restaurant_id} not found or has no menu"} + + return { + "restaurant_id": restaurant_id, + "menu_items": [ + { + "id": item.id, + "name": item.name, + "description": item.description, + "price": item.price, + "currency": item.currency, + "category": item.category, + "total_calories": item.total_calories, + "total_protein": item.total_protein, + "total_carbs": item.total_carbs, + "total_fat": item.total_fat, + "total_fiber": item.total_fiber, + "allergens": item.allergens, + "dietary_tags": item.dietary_tags, + "preparation_time": item.preparation_time, + "spice_level": item.spice_level, + "ingredients": [ + { + "name": ing.name, + "quantity": ing.quantity, + "calories_per_unit": ing.calories_per_unit, + "protein": ing.protein, + "carbs": ing.carbs, + "fat": ing.fat, + "fiber": ing.fiber, + "allergens": ing.allergens + } + for ing in item.ingredients + ] + } + for item in menu + ], + "total_items": len(menu) + } + +@mcp.tool() +def search_menu_items_tool( + restaurant_id: Optional[str] = None, + category: Optional[str] = None, + max_calories: Optional[float] = None, + dietary_tags: Optional[List[str]] = None, + allergen_free: Optional[List[str]] = None +) -> Dict[str, Any]: + """Search menu items with nutritional and dietary filters + + Args: + restaurant_id: Specific restaurant ID (optional) + category: Menu category (appetizer, main, dessert, beverage) + max_calories: Maximum calories per item + dietary_tags: Dietary requirements (vegetarian, vegan, gluten-free) + allergen_free: Allergens to avoid + + Returns: + Filtered menu items with nutritional data + """ + items = search_menu_items(restaurant_id, category, max_calories, dietary_tags, allergen_free) + + return { + "menu_items": [ + { + "id": item.id, + "name": item.name, + "description": item.description, + "price": item.price, + "currency": item.currency, + "category": item.category, + "total_calories": item.total_calories, + "total_protein": item.total_protein, + "total_carbs": item.total_carbs, + "total_fat": item.total_fat, + "total_fiber": item.total_fiber, + "allergens": item.allergens, + "dietary_tags": item.dietary_tags, + "spice_level": item.spice_level + } + for item in items + ], + "total_count": len(items), + "filters_applied": { + "restaurant_id": restaurant_id, + "category": category, + "max_calories": max_calories, + "dietary_tags": dietary_tags, + "allergen_free": allergen_free + } + } + +# Booking tools +@mcp.tool() +def book_restaurant_reservation( + restaurant_id: str, + customer_name: str, + customer_email: str, + customer_phone: str, + reservation_date: str, + reservation_time: str, + party_size: int, + special_requests: Optional[str] = None +) -> Dict[str, Any]: + """Book a restaurant reservation + + Args: + restaurant_id: ID of the restaurant + customer_name: Name of the customer + customer_email: Email address for confirmation + customer_phone: Phone number + reservation_date: Date in YYYY-MM-DD format + reservation_time: Time in HH:MM format + party_size: Number of people (1-20) + special_requests: Optional special requests + + Returns: + Booking confirmation details + """ + result = book_reservation( + restaurant_id, customer_name, customer_email, customer_phone, + reservation_date, reservation_time, party_size, special_requests + ) + + return { + "success": result.success, + "reservation_id": result.reservation_id, + "message": result.message, + "confirmation_details": result.confirmation_details + } + +@mcp.tool() +def get_reservation_details(reservation_id: str) -> Dict[str, Any]: + """Get details of a specific reservation + + Args: + reservation_id: Unique reservation ID + + Returns: + Reservation details + """ + reservation = get_reservation(reservation_id) + if not reservation: + return {"error": f"Reservation {reservation_id} not found"} + + return { + "id": reservation.id, + "restaurant_id": reservation.restaurant_id, + "customer_name": reservation.customer_name, + "customer_email": reservation.customer_email, + "customer_phone": reservation.customer_phone, + "reservation_date": reservation.reservation_date.isoformat(), + "reservation_time": reservation.reservation_time.isoformat(), + "party_size": reservation.party_size, + "special_requests": reservation.special_requests, + "status": reservation.status, + "created_at": reservation.created_at.isoformat() if reservation.created_at else None + } + +@mcp.tool() +def cancel_restaurant_reservation(reservation_id: str) -> Dict[str, Any]: + """Cancel a restaurant reservation + + Args: + reservation_id: Unique reservation ID + + Returns: + Cancellation confirmation + """ + result = cancel_reservation(reservation_id) + return { + "success": result.success, + "message": result.message + } + +@mcp.tool() +def get_restaurant_availability_tool(restaurant_id: str, date: str) -> Dict[str, Any]: + """Get available time slots for a restaurant on a specific date + + Args: + restaurant_id: ID of the restaurant + date: Date in YYYY-MM-DD format + + Returns: + Available time slots and capacity information + """ + return get_restaurant_availability(restaurant_id, date) + +# Resources +@mcp.resource("restaurants://{restaurant_id}") +def get_restaurant_resource(restaurant_id: str) -> str: + """Get restaurant information as a formatted resource""" + restaurant = get_restaurant_by_id(restaurant_id) + if not restaurant: + return f"Restaurant {restaurant_id} not found" + + result = f"🍽️ **{restaurant.name}**\n\n" + result += f"**Cuisine:** {restaurant.cuisine_type}\n" + result += f"**Location:** {restaurant.location}\n" + result += f"**Address:** {restaurant.address}\n" + result += f"**Phone:** {restaurant.phone}\n" + result += f"**Rating:** {restaurant.rating}/5.0\n" + result += f"**Price Range:** {restaurant.price_range}\n" + result += f"**Capacity:** {restaurant.capacity} people\n\n" + + result += "**Opening Hours:**\n" + for day, hours in restaurant.opening_hours.items(): + result += f"• {day}: {hours}\n" + + if restaurant.special_features: + result += f"\n**Special Features:** {', '.join(restaurant.special_features)}\n" + + return result + +@mcp.resource("restaurants://menu/{restaurant_id}") +def get_restaurant_menu_resource(restaurant_id: str) -> str: + """Get restaurant menu as a formatted resource""" + menu = get_restaurant_menu(restaurant_id) + if not menu: + return f"Menu not found for restaurant {restaurant_id}" + + result = f"🍽️ **Menu for Restaurant {restaurant_id}**\n\n" + + for item in menu: + result += f"**{item.name}** - {item.price} {item.currency}\n" + result += f"_{item.description}_\n" + result += f"📊 **Nutrition:** {item.total_calories} cal, {item.total_protein}g protein, {item.total_carbs}g carbs, {item.total_fat}g fat\n" + if item.allergens: + result += f"⚠️ **Allergens:** {', '.join(item.allergens)}\n" + if item.dietary_tags: + result += f"🏷️ **Dietary:** {', '.join(item.dietary_tags)}\n" + result += f"⏱️ **Prep Time:** {item.preparation_time} min | 🌶️ **Spice:** {item.spice_level}/5\n\n" + + return result + +# Prompts +@mcp.prompt() +def restaurant_booking_prompt(location: str, cuisine_type: Optional[str] = None) -> str: + """Generate a prompt for restaurant booking assistance""" + base_prompt = f"""Please help with restaurant booking in {location}""" + if cuisine_type: + base_prompt += f" specializing in {cuisine_type} cuisine" + + return base_prompt + """, including: +1. Restaurant recommendations based on preferences +2. Menu analysis with nutritional information +3. Dietary restrictions and allergen considerations +4. Booking availability and reservation process +5. Special requests and accommodations +6. Price range and budget considerations +7. Location and accessibility information +8. Integration with nutrition planning and macro tracking + +Focus on providing comprehensive restaurant guidance that integrates with nutrition and health goals.""" + +@mcp.prompt() +def nutrition_restaurant_prompt(restaurant_id: str, dietary_goals: Optional[str] = None) -> str: + """Generate a prompt for nutrition-focused restaurant guidance""" + base_prompt = f"""Please provide nutrition-focused guidance for restaurant {restaurant_id}""" + if dietary_goals: + base_prompt += f" with the goal of {dietary_goals}" + + return base_prompt + """, including: +1. Menu item analysis with detailed nutritional breakdown +2. Macro and micronutrient optimization +3. Calorie counting and portion control +4. Allergen and dietary restriction management +5. Integration with fitness and health goals +6. Meal planning and preparation timing +7. Supplement and nutrition timing considerations +8. Integration with other nutrition tracking services + +Focus on providing actionable nutrition advice that helps achieve specific health and fitness goals.""" + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/src/mcp/restaurants-mcp/models.py b/src/mcp/restaurants-mcp/models.py new file mode 100644 index 0000000..d1b5d66 --- /dev/null +++ b/src/mcp/restaurants-mcp/models.py @@ -0,0 +1,105 @@ +""" +Restaurant data models for the restaurants-mcp service. +""" + +from typing import List, Optional, Dict, Any +from dataclasses import dataclass +from datetime import datetime, date, time + + +@dataclass +class Ingredient: + """Represents an ingredient in a menu item.""" + name: str + quantity: str + calories_per_unit: float + protein: float # grams + carbs: float # grams + fat: float # grams + fiber: float # grams + allergens: List[str] = None + + def __post_init__(self): + if self.allergens is None: + self.allergens = [] + + +@dataclass +class MenuItem: + """Represents a menu item with nutritional information.""" + id: str + name: str + description: str + price: float + currency: str + category: str # appetizer, main, dessert, beverage + ingredients: List[Ingredient] + total_calories: float + total_protein: float + total_carbs: float + total_fat: float + total_fiber: float + allergens: List[str] + dietary_tags: List[str] # vegetarian, vegan, gluten-free, etc. + preparation_time: int # minutes + spice_level: int # 1-5 scale + + def __post_init__(self): + if self.allergens is None: + self.allergens = [] + if self.dietary_tags is None: + self.dietary_tags = [] + + +@dataclass +class Restaurant: + """Represents a restaurant with location, menu, and booking info.""" + id: str + name: str + description: str + cuisine_type: str + location: str # London or Paris + address: str + phone: str + email: str + rating: float + price_range: str # $, $$, $$$, $$$$ + opening_hours: Dict[str, str] # day -> hours + menu: List[MenuItem] + capacity: int + booking_advance_days: int # how many days in advance can book + special_features: List[str] # outdoor seating, live music, etc. + coordinates: Dict[str, float] # lat, lng + + def __post_init__(self): + if self.special_features is None: + self.special_features = [] + + +@dataclass +class Reservation: + """Represents a restaurant reservation.""" + id: str + restaurant_id: str + customer_name: str + customer_email: str + customer_phone: str + reservation_date: date + reservation_time: time + party_size: int + special_requests: Optional[str] = None + status: str = "confirmed" # confirmed, cancelled, completed + created_at: datetime = None + + def __post_init__(self): + if self.created_at is None: + self.created_at = datetime.now() + + +@dataclass +class BookingResponse: + """Response for booking operations.""" + success: bool + reservation_id: Optional[str] = None + message: str = "" + confirmation_details: Optional[Dict[str, Any]] = None \ No newline at end of file diff --git a/src/mcp/restaurants-mcp/pyproject.toml b/src/mcp/restaurants-mcp/pyproject.toml new file mode 100644 index 0000000..9fb7340 --- /dev/null +++ b/src/mcp/restaurants-mcp/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "restaurants-mcp" +version = "0.1.0" +description = "Restaurant discovery, menu browsing, and reservation booking MCP server" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mcp>=1.0.0", + "fastmcp>=0.1.0", + "dataclasses-json>=0.6.0" +] diff --git a/src/mcp/restaurants-mcp/restaurant_data.py b/src/mcp/restaurants-mcp/restaurant_data.py new file mode 100644 index 0000000..05e48df --- /dev/null +++ b/src/mcp/restaurants-mcp/restaurant_data.py @@ -0,0 +1,445 @@ +""" +Hardcoded restaurant data for London and Paris. +""" + +from models import Restaurant, MenuItem, Ingredient + +# London Restaurants +LONDON_RESTAURANTS = [ + Restaurant( + id="london_1", + name="The Ivy", + description="Classic British cuisine with modern twists in an elegant setting", + cuisine_type="British", + location="London", + address="1-5 West Street, London WC2H 9NQ", + phone="+44 20 7836 4751", + email="reservations@the-ivy.co.uk", + rating=4.5, + price_range="$$$$", + opening_hours={ + "Monday": "12:00-23:00", + "Tuesday": "12:00-23:00", + "Wednesday": "12:00-23:00", + "Thursday": "12:00-23:00", + "Friday": "12:00-23:00", + "Saturday": "12:00-23:00", + "Sunday": "12:00-22:00" + }, + menu=[ + MenuItem( + id="london_1_1", + name="Beef Wellington", + description="Premium beef fillet wrapped in puff pastry with mushroom duxelles", + price=45.00, + currency="GBP", + category="main", + ingredients=[ + Ingredient("beef fillet", "200g", 250, 45, 0, 15, 0, ["beef"]), + Ingredient("puff pastry", "100g", 350, 5, 35, 20, 1, ["gluten", "dairy"]), + Ingredient("mushrooms", "50g", 15, 2, 3, 0, 1), + Ingredient("prosciutto", "30g", 80, 6, 0, 6, 0, ["pork"]) + ], + total_calories=695, + total_protein=58, + total_carbs=38, + total_fat=41, + total_fiber=2, + allergens=["beef", "gluten", "dairy", "pork"], + dietary_tags=[], + preparation_time=45, + spice_level=1 + ), + MenuItem( + id="london_1_2", + name="Fish and Chips", + description="Traditional British fish and chips with mushy peas", + price=18.50, + currency="GBP", + category="main", + ingredients=[ + Ingredient("cod fillet", "180g", 200, 40, 0, 2, 0, ["fish"]), + Ingredient("potatoes", "200g", 160, 4, 35, 0, 3), + Ingredient("flour", "50g", 180, 5, 35, 1, 2, ["gluten"]), + Ingredient("peas", "80g", 60, 4, 10, 0, 4) + ], + total_calories=600, + total_protein=53, + total_carbs=80, + total_fat=3, + total_fiber=9, + allergens=["fish", "gluten"], + dietary_tags=[], + preparation_time=25, + spice_level=1 + ) + ], + capacity=120, + booking_advance_days=30, + special_features=["outdoor seating", "private dining", "wine cellar"], + coordinates={"lat": 51.5118, "lng": -0.1260} + ), + Restaurant( + id="london_2", + name="Dishoom", + description="Bombay-style cafe serving authentic Indian street food", + cuisine_type="Indian", + location="London", + address="12 Upper St Martin's Ln, London WC2H 9FB", + phone="+44 20 7420 9320", + email="reservations@dishoom.com", + rating=4.3, + price_range="$$", + opening_hours={ + "Monday": "08:00-23:00", + "Tuesday": "08:00-23:00", + "Wednesday": "08:00-23:00", + "Thursday": "08:00-23:00", + "Friday": "08:00-23:00", + "Saturday": "08:00-23:00", + "Sunday": "09:00-22:00" + }, + menu=[ + MenuItem( + id="london_2_1", + name="Black Daal", + description="Slow-cooked black lentils with butter and cream", + price=12.50, + currency="GBP", + category="main", + ingredients=[ + Ingredient("black lentils", "150g", 200, 15, 35, 1, 8), + Ingredient("butter", "20g", 150, 0, 0, 16, 0, ["dairy"]), + Ingredient("cream", "30ml", 100, 1, 1, 10, 0, ["dairy"]), + Ingredient("onions", "50g", 20, 1, 4, 0, 1), + Ingredient("garlic", "10g", 15, 1, 3, 0, 0) + ], + total_calories=485, + total_protein=18, + total_carbs=43, + total_fat=27, + total_fiber=9, + allergens=["dairy"], + dietary_tags=["vegetarian", "gluten-free"], + preparation_time=20, + spice_level=3 + ) + ], + capacity=80, + booking_advance_days=14, + special_features=["live music", "outdoor seating", "takeaway"], + coordinates={"lat": 51.5118, "lng": -0.1260} + ) +] + +# Paris Restaurants +PARIS_RESTAURANTS = [ + Restaurant( + id="paris_1", + name="L'Atelier de Joël Robuchon", + description="Michelin-starred French cuisine with innovative presentations", + cuisine_type="French", + location="Paris", + address="5 Rue de Montalembert, 75007 Paris", + phone="+33 1 42 22 56 56", + email="reservations@joelrobuchon.com", + rating=4.7, + price_range="$$$$", + opening_hours={ + "Monday": "19:00-22:30", + "Tuesday": "19:00-22:30", + "Wednesday": "19:00-22:30", + "Thursday": "19:00-22:30", + "Friday": "19:00-22:30", + "Saturday": "19:00-22:30", + "Sunday": "Closed" + }, + menu=[ + MenuItem( + id="paris_1_1", + name="Truffle Risotto", + description="Creamy risotto with black truffle and parmesan", + price=65.00, + currency="EUR", + category="main", + ingredients=[ + Ingredient("arborio rice", "120g", 420, 8, 90, 1, 2, ["gluten"]), + Ingredient("black truffle", "15g", 50, 2, 5, 2, 1), + Ingredient("parmesan", "40g", 160, 14, 0, 11, 0, ["dairy"]), + Ingredient("butter", "30g", 220, 0, 0, 24, 0, ["dairy"]), + Ingredient("white wine", "50ml", 35, 0, 1, 0, 0) + ], + total_calories=885, + total_protein=24, + total_carbs=96, + total_fat=38, + total_fiber=3, + allergens=["gluten", "dairy"], + dietary_tags=["vegetarian"], + preparation_time=35, + spice_level=1 + ) + ], + capacity=60, + booking_advance_days=60, + special_features=["michelin star", "wine pairing", "private chef"], + coordinates={"lat": 48.8566, "lng": 2.3522} + ), + Restaurant( + id="paris_2", + name="Le Comptoir du Relais", + description="Traditional French bistro with classic dishes and local atmosphere", + cuisine_type="French", + location="Paris", + address="9 Carrefour de l'Odéon, 75006 Paris", + phone="+33 1 44 27 07 97", + email="contact@comptoirdurelais.com", + rating=4.2, + price_range="$$$", + opening_hours={ + "Monday": "12:00-14:30, 19:00-22:30", + "Tuesday": "12:00-14:30, 19:00-22:30", + "Wednesday": "12:00-14:30, 19:00-22:30", + "Thursday": "12:00-14:30, 19:00-22:30", + "Friday": "12:00-14:30, 19:00-22:30", + "Saturday": "12:00-14:30, 19:00-22:30", + "Sunday": "Closed" + }, + menu=[ + MenuItem( + id="paris_2_1", + name="Coq au Vin", + description="Classic French chicken braised in red wine with mushrooms", + price=28.00, + currency="EUR", + category="main", + ingredients=[ + Ingredient("chicken thigh", "200g", 300, 35, 0, 18, 0, ["chicken"]), + Ingredient("red wine", "100ml", 85, 0, 2, 0, 0), + Ingredient("mushrooms", "80g", 20, 3, 4, 0, 2), + Ingredient("bacon", "40g", 180, 8, 0, 14, 0, ["pork"]), + Ingredient("onions", "60g", 25, 1, 6, 0, 1) + ], + total_calories=610, + total_protein=47, + total_carbs=12, + total_fat=32, + total_fiber=3, + allergens=["chicken", "pork"], + dietary_tags=["gluten-free"], + preparation_time=50, + spice_level=2 + ) + ], + capacity=45, + booking_advance_days=21, + special_features=["outdoor seating", "wine bar", "traditional atmosphere"], + coordinates={"lat": 48.8522, "lng": 2.3372} + ) +] + +# Rome Restaurants +ROME_RESTAURANTS = [ + Restaurant( + id="rome_1", + name="Roscioli", + description="Historic Roman trattoria serving traditional Roman cuisine and exceptional wine selection", + cuisine_type="Italian", + location="Rome", + address="Via dei Giubbonari, 21, 00186 Roma RM", + phone="+39 06 687 5287", + email="info@roscioli.com", + rating=4.6, + price_range="$$$", + opening_hours={ + "Monday": "12:00-15:00, 19:00-23:00", + "Tuesday": "12:00-15:00, 19:00-23:00", + "Wednesday": "12:00-15:00, 19:00-23:00", + "Thursday": "12:00-15:00, 19:00-23:00", + "Friday": "12:00-15:00, 19:00-23:00", + "Saturday": "12:00-15:00, 19:00-23:00", + "Sunday": "Closed" + }, + menu=[ + MenuItem( + id="rome_1_1", + name="Cacio e Pepe", + description="Classic Roman pasta with pecorino cheese and black pepper", + price=16.00, + currency="EUR", + category="main", + ingredients=[ + Ingredient("spaghetti", "120g", 420, 15, 85, 2, 3, ["gluten"]), + Ingredient("pecorino romano", "60g", 240, 18, 0, 18, 0, ["dairy"]), + Ingredient("black pepper", "5g", 15, 1, 3, 0, 1), + Ingredient("pasta water", "50ml", 0, 0, 0, 0, 0), + Ingredient("olive oil", "10ml", 90, 0, 0, 10, 0) + ], + total_calories=765, + total_protein=34, + total_carbs=88, + total_fat=30, + total_fiber=4, + allergens=["gluten", "dairy"], + dietary_tags=["vegetarian"], + preparation_time=15, + spice_level=2 + ), + MenuItem( + id="rome_1_2", + name="Saltimbocca alla Romana", + description="Veal cutlets with prosciutto and sage in white wine sauce", + price=28.00, + currency="EUR", + category="main", + ingredients=[ + Ingredient("veal cutlets", "200g", 300, 40, 0, 12, 0, ["beef"]), + Ingredient("prosciutto", "40g", 120, 8, 0, 10, 0, ["pork"]), + Ingredient("sage", "5g", 5, 0, 1, 0, 0), + Ingredient("white wine", "50ml", 35, 0, 1, 0, 0), + Ingredient("butter", "20g", 150, 0, 0, 16, 0, ["dairy"]), + Ingredient("flour", "20g", 70, 2, 15, 0, 1, ["gluten"]) + ], + total_calories=680, + total_protein=50, + total_carbs=17, + total_fat=38, + total_fiber=1, + allergens=["beef", "pork", "dairy", "gluten"], + dietary_tags=[], + preparation_time=25, + spice_level=1 + ), + MenuItem( + id="rome_1_3", + name="Tiramisu", + description="Classic Italian dessert with coffee-soaked ladyfingers and mascarpone", + price=8.50, + currency="EUR", + category="dessert", + ingredients=[ + Ingredient("ladyfingers", "80g", 280, 8, 50, 4, 2, ["gluten", "eggs"]), + Ingredient("mascarpone", "100g", 400, 6, 4, 40, 0, ["dairy"]), + Ingredient("espresso", "60ml", 2, 0, 0, 0, 0), + Ingredient("eggs", "2 large", 140, 12, 1, 10, 0, ["eggs"]), + Ingredient("sugar", "40g", 160, 0, 40, 0, 0), + Ingredient("cocoa powder", "10g", 20, 2, 4, 1, 2) + ], + total_calories=1002, + total_protein=28, + total_carbs=99, + total_fat=55, + total_fiber=4, + allergens=["gluten", "eggs", "dairy"], + dietary_tags=["vegetarian"], + preparation_time=20, + spice_level=1 + ) + ], + capacity=60, + booking_advance_days=21, + special_features=["wine cellar", "traditional atmosphere", "historic location"], + coordinates={"lat": 41.9028, "lng": 12.4964} + ), + Restaurant( + id="rome_2", + name="Armando al Pantheon", + description="Family-run trattoria near the Pantheon serving authentic Roman specialties", + cuisine_type="Italian", + location="Rome", + address="Salita dei Crescenzi, 31, 00186 Roma RM", + phone="+39 06 6880 3034", + email="info@armandoalpantheon.it", + rating=4.4, + price_range="$$", + opening_hours={ + "Monday": "12:30-14:30, 19:30-22:30", + "Tuesday": "12:30-14:30, 19:30-22:30", + "Wednesday": "12:30-14:30, 19:30-22:30", + "Thursday": "12:30-14:30, 19:30-22:30", + "Friday": "12:30-14:30, 19:30-22:30", + "Saturday": "12:30-14:30, 19:30-22:30", + "Sunday": "Closed" + }, + menu=[ + MenuItem( + id="rome_2_1", + name="Carbonara", + description="Traditional Roman pasta with eggs, pecorino, guanciale, and black pepper", + price=14.00, + currency="EUR", + category="main", + ingredients=[ + Ingredient("spaghetti", "120g", 420, 15, 85, 2, 3, ["gluten"]), + Ingredient("guanciale", "60g", 300, 8, 0, 30, 0, ["pork"]), + Ingredient("eggs", "2 large", 140, 12, 1, 10, 0, ["eggs"]), + Ingredient("pecorino romano", "40g", 160, 12, 0, 12, 0, ["dairy"]), + Ingredient("black pepper", "3g", 8, 0, 2, 0, 0) + ], + total_calories=1028, + total_protein=35, + total_carbs=88, + total_fat=52, + total_fiber=3, + allergens=["gluten", "pork", "eggs", "dairy"], + dietary_tags=[], + preparation_time=12, + spice_level=2 + ), + MenuItem( + id="rome_2_2", + name="Amatriciana", + description="Classic Roman pasta with tomato sauce, guanciale, and pecorino", + price=13.00, + currency="EUR", + category="main", + ingredients=[ + Ingredient("bucatini", "120g", 420, 15, 85, 2, 3, ["gluten"]), + Ingredient("guanciale", "50g", 250, 7, 0, 25, 0, ["pork"]), + Ingredient("tomato sauce", "100g", 20, 1, 4, 0, 1), + Ingredient("pecorino romano", "30g", 120, 9, 0, 9, 0, ["dairy"]), + Ingredient("onion", "30g", 12, 0, 3, 0, 0), + Ingredient("red pepper flakes", "2g", 6, 0, 1, 0, 0) + ], + total_calories=828, + total_protein=32, + total_carbs=94, + total_fat=36, + total_fiber=4, + allergens=["gluten", "pork", "dairy"], + dietary_tags=[], + preparation_time=18, + spice_level=3 + ), + MenuItem( + id="rome_2_3", + name="Suppli al Telefono", + description="Roman rice balls with mozzarella and tomato sauce, breaded and fried", + price=6.00, + currency="EUR", + category="appetizer", + ingredients=[ + Ingredient("arborio rice", "80g", 280, 6, 60, 0, 1), + Ingredient("mozzarella", "40g", 120, 8, 1, 9, 0, ["dairy"]), + Ingredient("tomato sauce", "30g", 6, 0, 1, 0, 0), + Ingredient("eggs", "1 large", 70, 6, 0, 5, 0, ["eggs"]), + Ingredient("breadcrumbs", "30g", 120, 4, 22, 2, 2, ["gluten"]), + Ingredient("olive oil", "20ml", 180, 0, 0, 20, 0) + ], + total_calories=776, + total_protein=24, + total_carbs=84, + total_fat=36, + total_fiber=3, + allergens=["dairy", "eggs", "gluten"], + dietary_tags=["vegetarian"], + preparation_time=30, + spice_level=1 + ) + ], + capacity=40, + booking_advance_days=14, + special_features=["family-run", "near Pantheon", "traditional recipes"], + coordinates={"lat": 41.8986, "lng": 12.4769} + ) +] diff --git a/src/mcp/restaurants-mcp/restaurant_service.py b/src/mcp/restaurants-mcp/restaurant_service.py new file mode 100644 index 0000000..a65b718 --- /dev/null +++ b/src/mcp/restaurants-mcp/restaurant_service.py @@ -0,0 +1,250 @@ +""" +Restaurant service with business logic for reservations and menu operations. +""" + +from typing import List, Dict, Any, Optional +from datetime import datetime, date, time, timedelta +import random +import string + +from models import Restaurant, MenuItem, Reservation, BookingResponse +from restaurant_data import LONDON_RESTAURANTS, PARIS_RESTAURANTS, ROME_RESTAURANTS + +# In-memory storage for reservations (in production, this would be a database) +reservations_db: List[Reservation] = [] + + +def get_restaurants_by_location(location: str) -> List[Restaurant]: + """Get restaurants by location (London, Paris, or Rome).""" + if location.lower() == "london": + return LONDON_RESTAURANTS + elif location.lower() == "paris": + return PARIS_RESTAURANTS + elif location.lower() == "rome": + return ROME_RESTAURANTS + else: + return LONDON_RESTAURANTS + PARIS_RESTAURANTS + ROME_RESTAURANTS + + +def get_restaurant_by_id(restaurant_id: str) -> Optional[Restaurant]: + """Get a specific restaurant by ID.""" + all_restaurants = LONDON_RESTAURANTS + PARIS_RESTAURANTS + ROME_RESTAURANTS + for restaurant in all_restaurants: + if restaurant.id == restaurant_id: + return restaurant + return None + + +def search_restaurants( + location: Optional[str] = None, + cuisine_type: Optional[str] = None, + price_range: Optional[str] = None, + rating_min: Optional[float] = None +) -> List[Restaurant]: + """Search restaurants with filters.""" + restaurants = get_restaurants_by_location(location) if location else LONDON_RESTAURANTS + PARIS_RESTAURANTS + ROME_RESTAURANTS + + filtered = restaurants + + if cuisine_type: + filtered = [r for r in filtered if cuisine_type.lower() in r.cuisine_type.lower()] + + if price_range: + filtered = [r for r in filtered if r.price_range == price_range] + + if rating_min: + filtered = [r for r in filtered if r.rating >= rating_min] + + return filtered + + +def get_restaurant_menu(restaurant_id: str) -> Optional[List[MenuItem]]: + """Get menu for a specific restaurant.""" + restaurant = get_restaurant_by_id(restaurant_id) + return restaurant.menu if restaurant else None + + +def search_menu_items( + restaurant_id: Optional[str] = None, + category: Optional[str] = None, + max_calories: Optional[float] = None, + dietary_tags: Optional[List[str]] = None, + allergen_free: Optional[List[str]] = None +) -> List[MenuItem]: + """Search menu items with filters.""" + all_items = [] + + if restaurant_id: + menu = get_restaurant_menu(restaurant_id) + if menu: + all_items = menu + else: + # Search all restaurants + for restaurant in LONDON_RESTAURANTS + PARIS_RESTAURANTS + ROME_RESTAURANTS: + all_items.extend(restaurant.menu) + + filtered = all_items + + if category: + filtered = [item for item in filtered if item.category.lower() == category.lower()] + + if max_calories: + filtered = [item for item in filtered if item.total_calories <= max_calories] + + if dietary_tags: + filtered = [item for item in filtered if any(tag in item.dietary_tags for tag in dietary_tags)] + + if allergen_free: + filtered = [item for item in filtered if not any(allergen in item.allergens for allergen in allergen_free)] + + return filtered + + +def book_reservation( + restaurant_id: str, + customer_name: str, + customer_email: str, + customer_phone: str, + reservation_date: str, + reservation_time: str, + party_size: int, + special_requests: Optional[str] = None +) -> BookingResponse: + """Book a restaurant reservation.""" + restaurant = get_restaurant_by_id(restaurant_id) + if not restaurant: + return BookingResponse( + success=False, + message=f"Restaurant with ID {restaurant_id} not found" + ) + + # Check if restaurant is open on that day + try: + date_obj = datetime.strptime(reservation_date, "%Y-%m-%d").date() + day_name = date_obj.strftime("%A") + if day_name not in restaurant.opening_hours or restaurant.opening_hours[day_name] == "Closed": + return BookingResponse( + success=False, + message=f"Restaurant is closed on {day_name}" + ) + except ValueError: + return BookingResponse( + success=False, + message="Invalid date format. Please use YYYY-MM-DD" + ) + + # Check advance booking limit + today = date.today() + if date_obj > today + timedelta(days=restaurant.booking_advance_days): + return BookingResponse( + success=False, + message=f"Can only book up to {restaurant.booking_advance_days} days in advance" + ) + + if date_obj < today: + return BookingResponse( + success=False, + message="Cannot book for past dates" + ) + + # Generate reservation ID + reservation_id = ''.join(random.choices(string.ascii_uppercase + string.digits, k=8)) + + # Create reservation + try: + time_obj = datetime.strptime(reservation_time, "%H:%M").time() + except ValueError: + return BookingResponse( + success=False, + message="Invalid time format. Please use HH:MM" + ) + + reservation = Reservation( + id=reservation_id, + restaurant_id=restaurant_id, + customer_name=customer_name, + customer_email=customer_email, + customer_phone=customer_phone, + reservation_date=date_obj, + reservation_time=time_obj, + party_size=party_size, + special_requests=special_requests + ) + + # Store reservation + reservations_db.append(reservation) + + return BookingResponse( + success=True, + reservation_id=reservation_id, + message="Reservation confirmed successfully", + confirmation_details={ + "restaurant_name": restaurant.name, + "restaurant_address": restaurant.address, + "restaurant_phone": restaurant.phone, + "reservation_date": reservation_date, + "reservation_time": reservation_time, + "party_size": party_size, + "special_requests": special_requests + } + ) + + +def get_reservation(reservation_id: str) -> Optional[Reservation]: + """Get reservation details by ID.""" + for reservation in reservations_db: + if reservation.id == reservation_id: + return reservation + return None + + +def cancel_reservation(reservation_id: str) -> BookingResponse: + """Cancel a reservation.""" + reservation = get_reservation(reservation_id) + if not reservation: + return BookingResponse( + success=False, + message=f"Reservation {reservation_id} not found" + ) + + reservation.status = "cancelled" + return BookingResponse( + success=True, + message="Reservation cancelled successfully" + ) + + +def get_restaurant_availability(restaurant_id: str, date_str: str) -> Dict[str, Any]: + """Get available time slots for a restaurant on a specific date.""" + restaurant = get_restaurant_by_id(restaurant_id) + if not restaurant: + return {"error": f"Restaurant {restaurant_id} not found"} + + try: + date_obj = datetime.strptime(date_str, "%Y-%m-%d").date() + day_name = date_obj.strftime("%A") + + if day_name not in restaurant.opening_hours or restaurant.opening_hours[day_name] == "Closed": + return {"error": f"Restaurant is closed on {day_name}"} + + # Simple availability check (in production, this would check actual bookings) + hours = restaurant.opening_hours[day_name] + if "12:00" in hours: + return { + "date": date_str, + "available_slots": [ + "12:00", "12:30", "13:00", "13:30", "14:00", + "19:00", "19:30", "20:00", "20:30", "21:00" + ], + "restaurant_capacity": restaurant.capacity + } + else: + return { + "date": date_str, + "available_slots": [ + "19:00", "19:30", "20:00", "20:30", "21:00", "21:30" + ], + "restaurant_capacity": restaurant.capacity + } + except ValueError: + return {"error": "Invalid date format. 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spotted at locations with juicy details +- **Insider Tips**: Best times to visit, secret spots, and local customs +- **In-Memory Database**: Fast access to curated secrets and tips + +## Available Attractions + +- `space_needle` - Seattle's iconic tower +- `pike_place` - Seattle's famous market +- `golden_gate_bridge` - San Francisco's landmark +- `central_park` - New York's urban oasis +- `times_square` - New York's bustling square + +## Tools + +### `get_attraction_secret(attraction_id)` + +Get a random secret, celebrity sighting, or insider tip for an attraction. + +### `get_insider_tip(attraction_id, tip_type?)` + +Get insider tips, optionally filtered by category (photo, food, timing, access, local). + +### `get_celebrity_drama(location)` + +Get celebrity sightings and drama for a location. + +### `get_all_attraction_secrets(attraction_id)` + +Get all secrets, celebrity sightings, and insider tips for an attraction. + +### `get_available_attractions()` + +Get list of all attractions with secrets available. + +## Resources + +- `trivia://attraction/{attraction_id}` - All secrets for an attraction +- `trivia://attractions` - List of available attractions + +## Prompts + +- `attraction_secrets_prompt(attraction_id)` - Discover attraction secrets +- `travel_secrets_prompt(location)` - Uncover location secrets +- `celebrity_hunting_prompt(location)` - Find celebrity drama + +## Running the Server + +```bash +uv run mcp dev main.py +``` + +The server will run on port 8012 by default. diff --git a/src/mcp/trivia-mcp/__init__.py b/src/mcp/trivia-mcp/__init__.py new file mode 100644 index 0000000..fc98e77 --- /dev/null +++ b/src/mcp/trivia-mcp/__init__.py @@ -0,0 +1,3 @@ +""" +Trivia MCP Server - Local secrets and insider information about attractions. +""" diff --git a/src/mcp/trivia-mcp/config.py b/src/mcp/trivia-mcp/config.py new file mode 100644 index 0000000..a4c5d4e --- /dev/null +++ b/src/mcp/trivia-mcp/config.py @@ -0,0 +1,18 @@ +""" +Nutrition Trivia MCP configuration and in-memory database. +""" + +# Keep the old database for backward compatibility (empty for now) +SECRET_DATABASE = {} + +# Tip categories for organized retrieval +TIP_CATEGORIES = { + "photo": "Photography tips and best spots", + "food": "Secret menu items and hidden restaurants", + "timing": "Best times to visit and avoid crowds", + "access": "Secret entrances and hidden areas", + "local": "Local customs and insider knowledge", +} + +# Secret types +SECRET_TYPES = ["secrets", "celebrity_sightings", "insider_tips"] \ No newline at end of file diff --git a/src/mcp/trivia-mcp/main.py b/src/mcp/trivia-mcp/main.py new file mode 100644 index 0000000..9a9e01c --- /dev/null +++ b/src/mcp/trivia-mcp/main.py @@ -0,0 +1,168 @@ +""" +Nutrition Trivia MCP Server - Nutrition secrets, celebrity facts, and insider tips. + +To run this server: + uv run mcp dev main.py + +Provides nutrition secrets, celebrity nutrition facts, and nutrition tips. +""" + +from typing import Dict, Any, Optional +from mcp.server.fastmcp import FastMCP + +from trivia_service import ( + # Nutrition trivia functions + get_nutrition_secret, + get_nutrition_tips, + get_celebrity_nutrition, + get_all_nutrition_secrets, + get_available_nutrition_topics, + format_nutrition_secrets, +) + +mcp = FastMCP("Trivia", port=8013) + + +# Nutrition trivia tools +@mcp.tool() +def get_nutrition_secret_tool(nutrition_topic: str) -> Dict[str, Any]: + """Get a random nutrition secret, celebrity fact, or nutrition tip for a topic + + Args: + nutrition_topic: ID of the nutrition topic (e.g., "protein", "carbs", "vitamins") + + Returns: + NutritionResponse object as dictionary with random nutrition secret/fact/tip + """ + return get_nutrition_secret(nutrition_topic) + + +@mcp.tool() +def get_nutrition_tips_tool( + nutrition_topic: str, tip_type: Optional[str] = None +) -> Dict[str, Any]: + """Get nutrition tips for a specific topic, optionally filtered by type + + Args: + nutrition_topic: ID of the nutrition topic + tip_type: Optional tip category filter ("macros", "vitamins", "hydration", etc.) + + Returns: + List of nutrition tips as dictionary + """ + return get_nutrition_tips(nutrition_topic, tip_type) + + +@mcp.tool() +def get_celebrity_nutrition_tool(nutrition_topic: str) -> Dict[str, Any]: + """Get celebrity nutrition facts and secrets for a topic + + Args: + nutrition_topic: Nutrition topic name (e.g., "protein", "carbs", "vitamins") + + Returns: + Celebrity nutrition facts as dictionary + """ + return get_celebrity_nutrition(nutrition_topic) + + +@mcp.tool() +def get_all_nutrition_secrets_tool(nutrition_topic: str) -> Dict[str, Any]: + """Get all nutrition secrets, celebrity facts, and tips for a topic + + Args: + nutrition_topic: ID of the nutrition topic + + Returns: + Complete NutritionSecret object as dictionary + """ + return get_all_nutrition_secrets(nutrition_topic) + + +@mcp.tool() +def get_available_nutrition_topics_tool() -> Dict[str, Any]: + """Get list of all nutrition topics with secrets available + + Returns: + List of available nutrition topics as dictionary + """ + return get_available_nutrition_topics() + + +# Nutrition trivia resources +@mcp.resource("nutrition://topic/{nutrition_topic}") +def get_nutrition_secrets_resource(nutrition_topic: str) -> str: + """Get all nutrition secrets for a topic as a formatted resource""" + return format_nutrition_secrets(nutrition_topic) + + +@mcp.resource("nutrition://topics") +def get_available_nutrition_topics_resource() -> str: + """Get list of available nutrition topics as a formatted resource""" + data = get_available_nutrition_topics() + if "error" in data: + return f"Error: {data['error']}" + + result = "🥗 **Available Nutrition Topics with Secrets**\n\n" + result += f"Total Topics: {data['total_topics']}\n\n" + + for topic in data["topics"]: + result += f"• **{topic.replace('_', ' ').title()}**\n" + + result += f"\n**Tip Categories:** {', '.join(data['tip_categories'])}\n" + result += f"**Secret Types:** {', '.join(data['secret_types'])}\n" + + return result + + +# Nutrition trivia prompts +@mcp.prompt() +def nutrition_secrets_prompt(nutrition_topic: str) -> str: + """Generate a prompt for discovering nutrition secrets""" + return f"""Please help discover and share nutrition secrets about {nutrition_topic.replace('_', ' ').title()}, including: +1. Scientific nutrition facts and research +2. Celebrity nutrition secrets and diets +3. Insider tips for optimal nutrition +4. Hidden benefits and surprising facts +5. Common myths and misconceptions +6. Best practices for health and performance +7. Nutrient timing and absorption secrets +8. Professional athlete and celebrity nutrition strategies + +Focus on providing insider knowledge that only nutrition experts and celebrities would know.""" + + +@mcp.prompt() +def celebrity_nutrition_prompt(nutrition_topic: str) -> str: + """Generate a prompt for discovering celebrity nutrition facts""" + return f"""Please help uncover celebrity nutrition secrets for {nutrition_topic.replace('_', ' ').title()}, including: +1. Celebrity diets and nutrition strategies +2. Famous people's nutrition secrets and routines +3. Celebrity nutrition drama and controversies +4. Professional athlete nutrition facts +5. Celebrity nutrition transformations +6. Famous people's supplement routines +7. Celebrity nutrition tips and tricks +8. Insider celebrity nutrition knowledge + +Share the kind of nutrition knowledge that makes you feel like a celebrity insider.""" + + +@mcp.prompt() +def nutrition_optimization_prompt(nutrition_topic: str) -> str: + """Generate a prompt for nutrition optimization and performance""" + return f"""Please help with nutrition optimization for {nutrition_topic.replace('_', ' ').title()}, including: +1. Performance-enhancing nutrition strategies +2. Scientific nutrition facts and research +3. Optimal nutrient timing and combinations +4. Nutrition for specific goals (muscle building, fat loss, performance) +5. Supplement strategies and recommendations +6. Nutrition myths and facts +7. Professional and celebrity nutrition secrets +8. Advanced nutrition techniques and tips + +Focus on the most effective nutrition strategies used by professionals and celebrities.""" + + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/src/mcp/trivia-mcp/models.py b/src/mcp/trivia-mcp/models.py new file mode 100644 index 0000000..e4ac74f --- /dev/null +++ b/src/mcp/trivia-mcp/models.py @@ -0,0 +1,29 @@ +""" +Nutrition Trivia data models - Data classes for nutrition trivia and secrets objects. +""" + +from dataclasses import dataclass +from typing import List, Optional + + +@dataclass +class NutritionSecret: + nutrition_topic: str + nutrition_secrets: List[str] + celebrity_nutrition: List[str] + nutrition_tips: List[str] + + +@dataclass +class NutritionResponse: + nutrition_topic: str + secret_type: str # "nutrition_secret", "celebrity_nutrition", "nutrition_tip" + content: str + category: Optional[str] = None + + +@dataclass +class NutritionTriviaDatabase: + """In-memory database structure for nutrition trivia data""" + + nutrition_topics: dict # nutrition_topic -> NutritionSecret diff --git a/src/mcp/trivia-mcp/nutrition_config.py b/src/mcp/trivia-mcp/nutrition_config.py new file mode 100644 index 0000000..6a2f808 --- /dev/null +++ b/src/mcp/trivia-mcp/nutrition_config.py @@ -0,0 +1,220 @@ +""" +Nutrition Trivia MCP configuration and in-memory database. +""" + +# Nutrition-focused trivia database +NUTRITION_TRIVIA_DATABASE = { + "protein": { + "nutrition_secrets": [ + "Your body can only absorb 25-35g of protein per meal - the rest gets stored as fat!", + "Plant proteins are incomplete - you need to combine grains with legumes for complete amino acids", + "Whey protein is absorbed 30% faster than casein, making it perfect for post-workout", + "Ancient bodybuilders used to eat 20+ eggs daily - modern science says 3-4 is optimal", + ], + "celebrity_nutrition": [ + "Dwayne 'The Rock' Johnson eats 6-7 meals daily, consuming 5,000+ calories", + "Arnold Schwarzenegger's secret: he never skipped breakfast, even during competitions", + "Serena Williams follows a plant-based diet but still dominates tennis", + "Chris Hemsworth's Thor diet: 4,000 calories daily with 40% protein", + ], + "nutrition_tips": [ + "Eat protein within 30 minutes post-workout for maximum muscle synthesis", + "Combine rice and beans for a complete protein - it's cheaper than meat!", + "Greek yogurt has 2x the protein of regular yogurt", + "Your body burns 20-30% of protein calories just digesting it (thermic effect)", + ], + }, + "carbs": { + "nutrition_secrets": [ + "Your brain uses 120g of glucose daily - that's why you feel foggy on low-carb diets", + "Resistant starch in cold potatoes acts like fiber and feeds good gut bacteria", + "White rice has a higher glycemic index than table sugar (GI 73 vs 65)", + "Ancient humans ate 100+ different plant foods - modern diets average just 12", + ], + "celebrity_nutrition": [ + "Tom Brady avoids nightshades (tomatoes, peppers) - claims they cause inflammation", + "LeBron James eats 5-6 meals daily, with carbs strategically timed around workouts", + "Venus Williams switched to raw vegan diet to manage her autoimmune condition", + "Conor McGregor's secret: sweet potatoes before fights for sustained energy", + ], + "nutrition_tips": [ + "Eat carbs 2-3 hours before workouts for optimal performance", + "Brown rice has 3x more fiber than white rice", + "Oats contain beta-glucan fiber that lowers cholesterol naturally", + "Your muscles can store 400-500g of glycogen - that's 1,600-2,000 calories!", + ], + }, + "fats": { + "nutrition_secrets": [ + "Your brain is 60% fat - that's why low-fat diets can cause depression", + "Coconut oil is 90% saturated fat but contains MCTs that boost metabolism", + "Omega-3s from fish reduce inflammation better than supplements", + "Avocados have more potassium than bananas and 20+ vitamins/minerals", + ], + "celebrity_nutrition": [ + "Gwyneth Paltrow's GOOP diet: bulletproof coffee with grass-fed butter daily", + "Mark Wahlberg eats 6 meals with healthy fats to maintain his physique", + "Jennifer Aniston's secret: she never skips her morning avocado toast", + "Hugh Jackman's Wolverine diet: 40% healthy fats for hormone production", + ], + "nutrition_tips": [ + "Eat fats with vegetables to absorb fat-soluble vitamins (A, D, E, K)", + "Olive oil loses nutrients when heated above 375°F - use for dressings", + "Nuts and seeds are nature's multivitamins - eat a handful daily", + "Your body needs fat to make hormones - low-fat diets can cause hormonal issues", + ], + }, + "vitamins": { + "nutrition_secrets": [ + "Vitamin D deficiency affects 1 billion people worldwide - it's the 'sunshine vitamin'", + "Vitamin C from food is absorbed 2x better than supplements", + "B12 deficiency can cause permanent nerve damage - vegans must supplement", + "Vitamin K2 directs calcium to bones instead of arteries - found in fermented foods", + ], + "celebrity_nutrition": [ + "Oprah takes 50+ supplements daily and credits them for her energy", + "Gwyneth Paltrow's vitamin routine costs $200+ monthly", + "Tom Brady avoids nightshades but loads up on vitamin C from citrus", + "Jennifer Lopez's secret: she gets vitamin D from 15 minutes of daily sun", + ], + "nutrition_tips": [ + "Take vitamin D with fat for better absorption", + "Vitamin C enhances iron absorption from plant foods", + "B vitamins work together - take a B-complex, not individual B vitamins", + "Your skin makes vitamin D from sunlight - but sunscreen blocks it completely", + ], + }, + "minerals": { + "nutrition_secrets": [ + "Magnesium deficiency affects 68% of Americans - it's needed for 300+ enzyme reactions", + "Zinc deficiency can cause loss of taste and smell - it's in oysters and pumpkin seeds", + "Iron from meat is absorbed 3x better than from plants", + "Calcium needs vitamin D and K2 to reach bones - not just calcium alone", + ], + "celebrity_nutrition": [ + "Cameron Diaz takes magnesium before bed for better sleep", + "Ryan Reynolds drinks bone broth daily for collagen and minerals", + "Gisele Bündchen's secret: she eats seaweed for iodine and trace minerals", + "Matthew McConaughey credits zinc for his immune system during filming", + ], + "nutrition_tips": [ + "Take iron with vitamin C for better absorption", + "Magnesium helps with sleep, muscle cramps, and anxiety", + "Zinc is crucial for immune function - oysters have the most", + "Sea salt contains 60+ trace minerals that table salt lacks", + ], + }, + "hydration": { + "nutrition_secrets": [ + "You lose 2-3 liters of water daily through breathing, sweating, and urination", + "Dehydration reduces cognitive performance by 20% - even mild dehydration matters", + "Coconut water has natural electrolytes - better than sports drinks", + "Your thirst mechanism weakens with age - older adults need to drink more", + ], + "celebrity_nutrition": [ + "Beyoncé drinks 1 gallon of water daily and credits it for her glowing skin", + "Ryan Reynolds starts each day with 32oz of water before coffee", + "Jennifer Aniston's secret: she adds lemon to water for vitamin C", + "Dwayne Johnson drinks 1.5 gallons daily during intense training", + ], + "nutrition_tips": [ + "Drink water 30 minutes before meals to avoid diluting stomach acid", + "Add a pinch of sea salt to water for better hydration", + "Your urine should be pale yellow - dark yellow means dehydration", + "Cold water is absorbed faster than room temperature water", + ], + }, + "gut_health": { + "nutrition_secrets": [ + "Your gut has 100 trillion bacteria - 10x more than human cells in your body", + "Fiber feeds good bacteria - they produce short-chain fatty acids that reduce inflammation", + "Probiotics can survive stomach acid if taken with food", + "Your gut microbiome affects your mood, weight, and immune system", + ], + "celebrity_nutrition": [ + "Gwyneth Paltrow takes probiotics daily and credits them for her energy", + "Tom Brady avoids gluten and dairy to reduce gut inflammation", + "Jennifer Lopez's secret: she eats fermented foods for gut health", + "Hugh Jackman drinks kombucha daily for probiotics", + ], + "nutrition_tips": [ + "Eat 30+ different plant foods weekly for diverse gut bacteria", + "Fermented foods like kimchi and sauerkraut are natural probiotics", + "Prebiotics (fiber) feed probiotics - eat both together", + "Your gut takes 2-4 weeks to adapt to dietary changes", + ], + }, + "metabolism": { + "nutrition_secrets": [ + "Your metabolism slows 2-3% per decade after age 30 - but you can reverse it", + "Muscle burns 6 calories per pound at rest - fat burns only 2 calories", + "Eating protein increases metabolism for 3-4 hours (thermic effect)", + "Cold exposure can boost metabolism by 15% through brown fat activation", + ], + "celebrity_nutrition": [ + "Chris Hemsworth takes ice baths daily to boost metabolism", + "Jennifer Lopez's secret: she does intermittent fasting to reset metabolism", + "Dwayne Johnson eats every 2-3 hours to keep metabolism high", + "Ryan Reynolds credits cold showers for his energy and metabolism", + ], + "nutrition_tips": [ + "Eat protein with every meal to maintain muscle mass", + "Strength training increases metabolism for 24-48 hours post-workout", + "Green tea can boost metabolism by 4-5% for 2-3 hours", + "Your metabolism is highest in the morning - eat your biggest meal then", + ], + }, + "weight_loss": { + "nutrition_secrets": [ + "You need a 3,500 calorie deficit to lose 1 pound of fat", + "Crash diets slow metabolism permanently - sustainable weight loss is 1-2 lbs/week", + "Sleep deprivation increases hunger hormones by 30%", + "Stress increases cortisol, which promotes belly fat storage", + ], + "celebrity_nutrition": [ + "Jennifer Aniston lost 30 lbs by cutting out processed foods completely", + "Chris Pratt's transformation: he ate 6 small meals daily with protein", + "Rebel Wilson's secret: she focused on building muscle, not just losing weight", + "Adele's weight loss: she followed a Mediterranean diet with portion control", + ], + "nutrition_tips": [ + "Eat protein first to reduce blood sugar spikes and hunger", + "Drink water before meals to reduce calorie intake by 20%", + "Chew food 20+ times to increase satiety hormones", + "Your body burns more calories digesting protein than carbs or fat", + ], + }, + "performance": { + "nutrition_secrets": [ + "Carb loading works best when you taper training 3 days before competition", + "Caffeine can improve performance by 3-5% - but tolerance builds quickly", + "Beet juice increases nitric oxide, improving endurance by 2-3%", + "Your body can store 2,000 calories of glycogen - enough for 2 hours of intense exercise", + ], + "celebrity_nutrition": [ + "Serena Williams eats bananas during matches for quick energy", + "LeBron James drinks beet juice daily for performance", + "Tom Brady's secret: he avoids inflammatory foods before games", + "Conor McGregor loads carbs 3 days before fights for maximum energy", + ], + "nutrition_tips": [ + "Eat carbs 2-3 hours before training for sustained energy", + "Protein within 30 minutes post-workout maximizes muscle building", + "Hydration affects performance more than most people realize", + "Your body needs 1g protein per pound of bodyweight for muscle building", + ], + }, +} + +NUTRITION_TIP_CATEGORIES = { + "macros": "Macronutrient tips and secrets", + "vitamins": "Vitamin and mineral insights", + "hydration": "Water and electrolyte balance", + "gut_health": "Digestive health and microbiome", + "performance": "Sports nutrition and energy", + "weight_management": "Weight loss and gain strategies", + "celebrity": "Celebrity nutrition secrets", + "science": "Scientific nutrition facts", +} + +NUTRITION_SECRET_TYPES = ["nutrition_secrets", "celebrity_nutrition", "nutrition_tips"] diff --git a/src/mcp/trivia-mcp/pyproject.toml b/src/mcp/trivia-mcp/pyproject.toml new file mode 100644 index 0000000..5b2fdcb --- /dev/null +++ b/src/mcp/trivia-mcp/pyproject.toml @@ -0,0 +1,9 @@ +[project] +name = "trivia-mcp" +version = "1.0.0" +description = "MCP server for local secrets and insider information about attractions" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mcp[cli]>=1.13.1", +] diff --git a/src/mcp/trivia-mcp/trivia_service.py b/src/mcp/trivia-mcp/trivia_service.py new file mode 100644 index 0000000..1f8ab0d --- /dev/null +++ b/src/mcp/trivia-mcp/trivia_service.py @@ -0,0 +1,246 @@ +""" +Nutrition Trivia service with MCP tools for nutrition secrets and insider information. +""" + +import random +from typing import Dict, Any, List, Optional +from dataclasses import asdict + +from nutrition_config import NUTRITION_TRIVIA_DATABASE, NUTRITION_TIP_CATEGORIES, NUTRITION_SECRET_TYPES +from models import NutritionSecret, NutritionResponse + +try: + from config import SECRET_DATABASE, TIP_CATEGORIES, SECRET_TYPES +except ImportError: + SECRET_DATABASE = {} + TIP_CATEGORIES = {} + SECRET_TYPES = [] + + +# Nutrition trivia functions +def get_nutrition_secret(nutrition_topic: str) -> Dict[str, Any]: + """Get a random nutrition secret, celebrity fact, or nutrition tip for a topic + + Args: + nutrition_topic: ID of the nutrition topic (e.g., "protein", "carbs", "vitamins") + + Returns: + NutritionResponse object as dictionary or error dict + """ + try: + nutrition_topic = nutrition_topic.lower().replace(" ", "_") + + if nutrition_topic not in NUTRITION_TRIVIA_DATABASE: + return { + "error": f"Nutrition topic '{nutrition_topic}' not found in our nutrition database" + } + + topic_data = NUTRITION_TRIVIA_DATABASE[nutrition_topic] + + # Collect all available content + all_content = [] + for secret_type in NUTRITION_SECRET_TYPES: + if secret_type in topic_data: + for content in topic_data[secret_type]: + all_content.append({"type": secret_type, "content": content}) + + if not all_content: + return {"error": f"No nutrition secrets found for topic '{nutrition_topic}'"} + + # Pick a random item + random_item = random.choice(all_content) + + nutrition_response = NutritionResponse( + nutrition_topic=nutrition_topic, + secret_type=random_item["type"], + content=random_item["content"], + category=random_item["type"], + ) + + return asdict(nutrition_response) + + except Exception as e: + return {"error": f"Failed to get nutrition secret: {str(e)}"} + + +def get_nutrition_tips( + nutrition_topic: str, tip_type: Optional[str] = None +) -> Dict[str, Any]: + """Get nutrition tips for a specific topic, optionally filtered by type + + Args: + nutrition_topic: ID of the nutrition topic + tip_type: Optional tip category filter ("macros", "vitamins", "hydration", etc.) + + Returns: + List of nutrition tips as dictionary or error dict + """ + try: + nutrition_topic = nutrition_topic.lower().replace(" ", "_") + + if nutrition_topic not in NUTRITION_TRIVIA_DATABASE: + return { + "error": f"Nutrition topic '{nutrition_topic}' not found in our nutrition database" + } + + topic_data = NUTRITION_TRIVIA_DATABASE[nutrition_topic] + + if "nutrition_tips" not in topic_data: + return {"error": f"No nutrition tips found for topic '{nutrition_topic}'"} + + tips = topic_data["nutrition_tips"] + + # If tip_type is specified, try to filter (this is a simple keyword match) + if tip_type: + tip_type = tip_type.lower() + filtered_tips = [] + for tip in tips: + if tip_type in tip.lower(): + filtered_tips.append(tip) + + if filtered_tips: + tips = filtered_tips + else: + return { + "nutrition_topic": nutrition_topic, + "tip_type": tip_type, + "tips": [], + "message": f"No tips found matching category '{tip_type}'", + } + + return { + "nutrition_topic": nutrition_topic, + "tip_type": tip_type or "all", + "tips": tips, + "total_count": len(tips), + } + + except Exception as e: + return {"error": f"Failed to get nutrition tips: {str(e)}"} + + +def get_celebrity_nutrition(location: str) -> Dict[str, Any]: + """Get celebrity nutrition facts and secrets for a topic + + Args: + location: Nutrition topic name (e.g., "protein", "carbs", "vitamins") + + Returns: + Celebrity nutrition facts as dictionary or error dict + """ + try: + location = location.lower().replace(" ", "_") + + if location not in NUTRITION_TRIVIA_DATABASE: + return {"error": f"Nutrition topic '{location}' not found in our nutrition database"} + + topic_data = NUTRITION_TRIVIA_DATABASE[location] + + if "celebrity_nutrition" not in topic_data: + return {"error": f"No celebrity nutrition facts found for topic '{location}'"} + + celebrity_facts = topic_data["celebrity_nutrition"] + + # Get a random celebrity fact + random_fact = random.choice(celebrity_facts) if celebrity_facts else None + + return { + "nutrition_topic": location, + "celebrity_fact": random_fact, + "all_facts": celebrity_facts, + "total_count": len(celebrity_facts), + } + + except Exception as e: + return {"error": f"Failed to get celebrity nutrition: {str(e)}"} + + +def get_all_nutrition_secrets(nutrition_topic: str) -> Dict[str, Any]: + """Get all nutrition secrets, celebrity facts, and tips for a topic + + Args: + nutrition_topic: ID of the nutrition topic + + Returns: + Complete NutritionSecret object as dictionary or error dict + """ + try: + nutrition_topic = nutrition_topic.lower().replace(" ", "_") + + if nutrition_topic not in NUTRITION_TRIVIA_DATABASE: + return { + "error": f"Nutrition topic '{nutrition_topic}' not found in our nutrition database" + } + + topic_data = NUTRITION_TRIVIA_DATABASE[nutrition_topic] + + nutrition_secret = NutritionSecret( + nutrition_topic=nutrition_topic, + nutrition_secrets=topic_data.get("nutrition_secrets", []), + celebrity_nutrition=topic_data.get("celebrity_nutrition", []), + nutrition_tips=topic_data.get("nutrition_tips", []), + ) + + return asdict(nutrition_secret) + + except Exception as e: + return {"error": f"Failed to get nutrition secrets: {str(e)}"} + + +def get_available_nutrition_topics() -> Dict[str, Any]: + """Get list of all nutrition topics with secrets available + + Returns: + List of available nutrition topics as dictionary + """ + try: + topics = list(NUTRITION_TRIVIA_DATABASE.keys()) + + return { + "total_topics": len(topics), + "topics": topics, + "tip_categories": list(NUTRITION_TIP_CATEGORIES.keys()), + "secret_types": NUTRITION_SECRET_TYPES, + } + + except Exception as e: + return {"error": f"Failed to get available nutrition topics: {str(e)}"} + + +def format_nutrition_secrets(nutrition_topic: str) -> str: + """Format all nutrition secrets for a topic as a readable string + + Args: + nutrition_topic: ID of the nutrition topic + + Returns: + Formatted string with all nutrition secrets + """ + try: + data = get_all_nutrition_secrets(nutrition_topic) + if "error" in data: + return f"Error: {data['error']}" + + result = f"🥗 **Nutrition Secrets for {nutrition_topic.replace('_', ' ').title()}**\n\n" + + if data.get("nutrition_secrets"): + result += "🔬 **Nutrition Secrets:**\n" + for i, secret in 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100644 --- a/src/mcp/weather-mcp/README.md +++ b/src/mcp/weather-mcp/README.md @@ -5,6 +5,7 @@ A Model Context Protocol (MCP) server for getting comprehensive weather informat ## Features 🌤️ **Current Weather** + - Real-time weather conditions - Temperature with "feels like" values - Wind speed and direction @@ -12,6 +13,7 @@ A Model Context Protocol (MCP) server for getting comprehensive weather informat - Precipitation information 🌦️ **Weather Forecasts** + - 1-16 day forecasts - Daily temperature ranges (min/max) - Apparent temperature forecasts @@ -20,6 +22,7 @@ A Model Context Protocol (MCP) server for getting comprehensive weather informat - Weather condition descriptions 🌍 **Global Coverage** + - Worldwide location support - Automatic coordinate resolution - Timezone-aware data @@ -44,19 +47,22 @@ uv run mcp dev main.py ``` ```bash -# use this when wanting to consume within an agent +# use this when wanting to consume within an agent (HTTP on port 8009) uv run main.py ``` ### Available Tools #### 1. Get Current Weather + ```python get_current_weather(location: str) ``` + Get comprehensive current weather information including temperature, conditions, wind, humidity, and pressure. **Example:** + ```python get_current_weather("London") get_current_weather("New York") @@ -64,12 +70,15 @@ get_current_weather("Tokyo") ``` #### 2. Get Weather Forecast + ```python get_weather_forecast(location: str, days: int = 7) ``` + Get detailed weather forecast for 1-16 days with daily breakdowns. **Example:** + ```python get_weather_forecast("Paris", days=5) get_weather_forecast("Sydney", days=14) @@ -78,9 +87,11 @@ get_weather_forecast("Sydney", days=14) ### Resources Access weather data as resources: + - `weather://{location}` - Current weather formatted as text resource **Example:** + ``` weather://London weather://San Francisco @@ -89,73 +100,78 @@ weather://San Francisco ### Prompts #### 1. Weather Summary Prompt + ```python weather_summary_prompt(location: str, include_forecast: bool = False) ``` + Generate prompts for weather summaries with practical advice. **Examples:** + - `weather_summary_prompt("London")` - Current conditions with advice - `weather_summary_prompt("Tokyo", include_forecast=True)` - 3-day forecast with activities ## Data Structure ### Current Weather Response + ```json { - "location": "London, England, United Kingdom", - "coordinates": {"lat": 51.5074, "lon": -0.1278}, - "temperature": { - "current": 18.5, - "feels_like": 17.2, - "unit": "°C" - }, - "weather": { - "description": "Partly cloudy", - "code": 2 - }, - "wind": { - "speed": 12.0, - "direction": 240.0, - "unit": "km/h" - }, - "humidity": 65, - "pressure": 1013.2, - "precipitation": 0, - "timezone": "Europe/London", - "timestamp": "2024-01-15T14:30" + "location": "London, England, United Kingdom", + "coordinates": { "lat": 51.5074, "lon": -0.1278 }, + "temperature": { + "current": 18.5, + "feels_like": 17.2, + "unit": "°C" + }, + "weather": { + "description": "Partly cloudy", + "code": 2 + }, + "wind": { + "speed": 12.0, + "direction": 240.0, + "unit": "km/h" + }, + "humidity": 65, + "pressure": 1013.2, + "precipitation": 0, + "timezone": "Europe/London", + "timestamp": "2024-01-15T14:30" } ``` ### Forecast Response + ```json { - "location": "Paris, Île-de-France, France", - "coordinates": {"lat": 48.8566, "lon": 2.3522}, - "timezone": "Europe/Paris", - "forecast_days": 7, - "forecasts": [ - { - "date": "2024-01-15", - "temperature": {"min": 8.2, "max": 15.7, "unit": "°C"}, - "apparent_temperature": {"min": 6.1, "max": 14.2, "unit": "°C"}, - "weather": {"description": "Light rain", "code": 61}, - "precipitation": { - "total": 2.4, - "rain": 2.4, - "showers": 0, - "snow": 0, - "hours": 3, - "unit": "mm" - }, - "wind": { - "speed": 18.5, - "direction": 225.0, - "max_gusts": 32.1, - "unit": "km/h" - } - } - ] + "location": "Paris, Île-de-France, France", + "coordinates": { "lat": 48.8566, "lon": 2.3522 }, + "timezone": "Europe/Paris", + "forecast_days": 7, + "forecasts": [ + { + "date": "2024-01-15", + "temperature": { "min": 8.2, "max": 15.7, "unit": "°C" }, + "apparent_temperature": { "min": 6.1, "max": 14.2, "unit": "°C" }, + "weather": { "description": "Light rain", "code": 61 }, + "precipitation": { + "total": 2.4, + "rain": 2.4, + "showers": 0, + "snow": 0, + "hours": 3, + "unit": "mm" + }, + "wind": { + "speed": 18.5, + "direction": 225.0, + "max_gusts": 32.1, + "unit": "km/h" + } + } + ] } ``` @@ -163,16 +179,16 @@ Generate prompts for weather summaries with practical advice. The API uses WMO Weather interpretation codes: -| Code | Description | -|------|-------------| -| 0 | Clear sky | -| 1-3 | Mainly clear to overcast | -| 45-48 | Fog | +| Code | Description | +| ----- | --------------------------- | +| 0 | Clear sky | +| 1-3 | Mainly clear to overcast | +| 45-48 | Fog | | 51-57 | Drizzle (light to freezing) | -| 61-67 | Rain (slight to freezing) | -| 71-77 | Snow (slight to heavy) | -| 80-86 | Rain/snow showers | -| 95-99 | Thunderstorms | +| 61-67 | Rain (slight to freezing) | +| 71-77 | Snow (slight to heavy) | +| 80-86 | Rain/snow showers | +| 95-99 | Thunderstorms | ## Example Usage @@ -206,6 +222,7 @@ src/mcp/weather-mcp/ ## API Integration This MCP integrates with the **Open-Meteo API**, providing: + - **Free access** - No API key required - **Global coverage** - Weather data worldwide - **Reliable data** - Professional weather service @@ -216,6 +233,7 @@ This MCP integrates with the **Open-Meteo API**, providing: ## Features by Module ### Models (`models.py`) + - `Coordinates` - Latitude/longitude pairs - `Location` - Geographic location with timezone - `Temperature` - Current, min, max, feels-like values @@ -227,17 +245,20 @@ This MCP integrates with the **Open-Meteo API**, providing: - `WeatherForecast` - Multi-day forecast collection ### Configuration (`config.py`) + - API base URLs for weather and geocoding - Complete WMO weather code mappings - Standard units and formats ### Utilities (`utils.py`) + - `make_api_request()` - HTTP request handling with timeouts - `get_coordinates()` - Location name to coordinates conversion - `format_location_name()` - Pretty location formatting - `get_weather_description()` - Weather code to description mapping ### Weather Service (`weather_service.py`) + - `get_current_weather_data()` - Current conditions processing - `get_weather_forecast_data()` - Forecast data processing - `format_weather_resource()` - Resource text formatting @@ -246,8 +267,9 @@ This MCP integrates with the **Open-Meteo API**, providing: ## Development The codebase follows a modular structure with clear separation of concerns: + - **Models**: Data structures and type definitions - **Config**: Constants and API configuration - **Utils**: Reusable helper functions - **Service**: Core business logic -- **Main**: MCP server orchestration \ No newline at end of file +- **Main**: MCP server orchestration