Add example client to examples/clients folder

examples/clients/simple-chatbot/.python-version

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+3.10

examples/clients/simple-chatbot/README.MD

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+# MCP Simple Chatbot
+
+This example demonstrates how to integrate the Model Context Protocol (MCP) into a simple CLI chatbot. The implementation showcases MCP's flexibility by supporting multiple tools through MCP servers and is compatible with any LLM provider that follows OpenAI API standards.
+
+## Requirements
+
+- Python 3.10
+- `python-dotenv`
+- `requests`
+- `mcp`
+- `uvicorn`
+
+## Installation
+
+1. **Install the dependencies:**
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. **Set up environment variables:**
+
+   Create a `.env` file in the root directory and add your API key:
+
+   ```plaintext
+   LLM_API_KEY=your_api_key_here
+   ```
+
+3. **Configure servers:**
+
+   The `servers_config.json` follows the same structure as Claude Desktop, allowing for easy integration of multiple servers. 
+   Here's an example:
+
+   ```json
+   {
+     "mcpServers": {
+       "sqlite": {
+         "command": "uvx",
+         "args": ["mcp-server-sqlite", "--db-path", "./test.db"]
+       },
+       "puppeteer": {
+         "command": "npx",
+         "args": ["-y", "@modelcontextprotocol/server-puppeteer"]
+       }
+     }
+   }
+   ```
+   Environment variables are supported as well. Pass them as you would with the Claude Desktop App.
+
+   Example:
+   ```json
+   {
+     "mcpServers": {
+       "server_name": {
+         "command": "uvx",
+         "args": ["mcp-server-name", "--additional-args"],
+         "env": {
+           "API_KEY": "your_api_key_here"
+         }
+       }
+     }
+   }
+   ```
+
+## Usage
+
+1. **Run the client:**
+
+   ```bash
+   python main.py
+   ```
+
+2. **Interact with the assistant:**
+
+   The assistant will automatically detect available tools and can respond to queries based on the tools provided by the configured servers.
+
+3. **Exit the session:**
+
+   Type `quit` or `exit` to end the session.
+
+## Architecture
+
+- **Tool Discovery**: Tools are automatically discovered from configured servers.
+- **System Prompt**: Tools are dynamically included in the system prompt, allowing the LLM to understand available capabilities.
+- **Server Integration**: Supports any MCP-compatible server, tested with various server implementations including Uvicorn and Node.js.
+
+### Class Structure
+- **Configuration**: Manages environment variables and server configurations
+- **Server**: Handles MCP server initialization, tool discovery, and execution
+- **Tool**: Represents individual tools with their properties and formatting
+- **LLMClient**: Manages communication with the LLM provider
+- **ChatSession**: Orchestrates the interaction between user, LLM, and tools
+
+### Logic Flow
+
+1. **Tool Integration**:
+   - Tools are dynamically discovered from MCP servers
+   - Tool descriptions are automatically included in system prompt
+   - Tool execution is handled through standardized MCP protocol
+
+2. **Runtime Flow**:
+   - User input is received
+   - Input is sent to LLM with context of available tools
+   - LLM response is parsed:
+     - If it's a tool call → execute tool and return result
+     - If it's a direct response → return to user
+   - Tool results are sent back to LLM for interpretation
+   - Final response is presented to user
+
+

examples/clients/simple-chatbot/mcp_simple_chatbot/.env.example

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+GROQ_API_KEY=gsk_1234567890