Deployment: Dockerfile and Smithery config

Dockerfile

@@ -0,0 +1,39 @@
+# Generated by https://smithery.ai. See: https://smithery.ai/docs/config#dockerfile
+# Use a Python image with uv pre-installed
+FROM ghcr.io/astral-sh/uv:python3.10-bullseye-slim AS uv
+
+# Install the project into /app
+WORKDIR /app
+
+# Enable bytecode compilation
+ENV UV_COMPILE_BYTECODE=1
+
+# Copy from the cache instead of linking since it's a mounted volume
+ENV UV_LINK_MODE=copy
+
+# Install the project's dependencies using the lockfile and settings
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=uv.lock,target=uv.lock \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    uv sync --frozen --no-install-project --no-dev --no-editable
+
+# Then, add the rest of the project source code and install it
+# Installing separately from its dependencies allows optimal layer caching
+ADD . /app
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --frozen --no-dev --no-editable
+
+FROM python:3.10-slim-bullseye
+
+WORKDIR /app
+
+COPY --from=uv /root/.local /root/.local
+COPY --from=uv --chown=app:app /app/.venv /app/.venv
+
+# Place executables in the environment at the front of the path
+ENV PATH="/app/.venv/bin:$PATH"
+
+# Entry point for running the MCP server
+ENTRYPOINT ["uv", "run", "mcp-server-qdrant"]
+
+# To use the server, pass required arguments like --qdrant-url, --collection-name, etc.

README.md

@@ -1,5 +1,5 @@
 # mcp-server-qdrant: A Qdrant MCP server
-[![smithery badge](https://smithery.ai/badge/mcp-server-qdrant)](https://smithery.ai/protocol/mcp-server-qdrant)
+[![smithery badge](https://smithery.ai/badge/mcp-server-qdrant)](https://smithery.ai/server/mcp-server-qdrant)
 
 > The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
 
@@ -41,10 +41,10 @@ uv run mcp-server-qdrant \
 
 ### Installing via Smithery
 
-To install Qdrant MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/protocol/mcp-server-qdrant):
+To install Qdrant MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/mcp-server-qdrant):
 
 ```bash
-npx @smithery/cli install mcp-server-qdrant --client claude
+npx -y @smithery/cli install mcp-server-qdrant --client claude
 ```
 
 ## Usage with Claude Desktop

smithery.yaml

@@ -0,0 +1,32 @@
+# Smithery configuration file: https://smithery.ai/docs/config#smitheryyaml
+
+startCommand:
+  type: stdio
+  configSchema:
+    # JSON Schema defining the configuration options for the MCP.
+    type: object
+    required:
+      - collectionName
+    properties:
+      qdrantUrl:
+        type: string
+        description: The URL of the Qdrant server. Cannot be used together with
+          qdrantLocalPath.
+      qdrantLocalPath:
+        type: string
+        description: The local path to the Qdrant database. Cannot be used together with
+          qdrantUrl.
+      qdrantApiKey:
+        type: string
+        description: The API key for the Qdrant server.
+      collectionName:
+        type: string
+        description: The name of the collection to use or create.
+      fastembedModelName:
+        type: string
+        default: sentence-transformers/all-MiniLM-L6-v2
+        description: The embedding model to use.
+  commandFunction:
+    # A function that produces the CLI command to start the MCP on stdio.
+    |-
+    (config) => { const args = ['uv', 'run', 'mcp-server-qdrant']; if(config.qdrantUrl) { args.push('--qdrant-url', config.qdrantUrl); } if(config.qdrantLocalPath) { args.push('--qdrant-local-path', config.qdrantLocalPath); } if(config.qdrantApiKey) { args.push('--qdrant-api-key', config.qdrantApiKey); } args.push('--collection-name', config.collectionName); args.push('--fastembed-model-name', config.fastembedModelName || 'sentence-transformers/all-MiniLM-L6-v2'); return { command: 'uv', args: args }; }