FastMCP

# FastMCP - Model Context Protocol Server A lightweight Model Context Protocol (MCP) server implemented with [FastMCP](https://github.com/jlowin/fastmcp), a fast and Pythonic framework for building MCP servers and clients. ## Features - Create, retrieve, update, and delete model contexts - Query execution against specific contexts - Filtering by model name and tags - In-memory storage (for development) - FastMCP integration for easy MCP server development - Datadog integration for metrics and monitoring ## Requirements - Python 3.7+ - FastMCP - uv (recommended for environment management) - Datadog account (optional, for metrics) ## Installation ### Using uv (Recommended) The simplest way to install is using the provided scripts: #### Unix/Linux/macOS ```bash # Clone the repository git clone https://github.com/yourusername/datadog-mcp-server.git cd datadog-mcp-server # Make the install script executable chmod +x install.sh # Run the installer ./install.sh ``` #### Windows ```powershell # Clone the repository git clone https://github.com/yourusername/datadog-mcp-server.git cd datadog-mcp-server # Run the installer .\install.ps1 ``` ### Manual Installation ```bash # Clone the repository git clone https://github.com/yourusername/datadog-mcp-server.git cd datadog-mcp-server # Create and activate a virtual environment with uv uv venv # On Unix/Linux/macOS: source .venv/bin/activate # On Windows: .\.venv\Scripts\activate # Install dependencies uv pip install -r requirements.txt ``` ## Datadog Configuration The server integrates with Datadog for metrics and monitoring. You can configure Datadog API credentials in several ways: ### 1. Environment Variables Set these environment variables before starting the server: ```bash # Unix/Linux/macOS export DATADOG_API_KEY=your_api_key export DATADOG_APP_KEY=your_app_key # Optional export DATADOG_SITE=datadoghq.com # Optional, default: datadoghq.com # Windows PowerShell $env:DATADOG_API_KEY = 'your_api_key' $env:DATADOG_APP_KEY = 'your_app_key' # Optional $env:DATADOG_SITE = 'datadoghq.com' # Optional ``` ### 2. .env File Create a `.env` file in the project directory: ``` DATADOG_API_KEY=your_api_key DATADOG_APP_KEY=your_app_key DATADOG_SITE=datadoghq.com ``` ### 3. FastMCP CLI Installation When installing as a Claude Desktop tool, you can pass environment variables: ```bash fastmcp install mcp_server.py --name "Model Context Server" -v DATADOG_API_KEY=your_api_key ``` ### 4. Runtime Configuration Use the `configure_datadog` tool at runtime: ```python result = await client.call_tool("configure_datadog", { "api_key": "your_api_key", "app_key": "your_app_key", # Optional "site": "datadoghq.com" # Optional }) ``` ## Usage ### Starting the Server ```bash # Start directly from activated environment python mcp_server.py # Or use uv run (no activation needed) uv run python mcp_server.py # Use FastMCP CLI for development (if in activated environment) fastmcp dev mcp_server.py # Use FastMCP CLI with uv (no activation needed) uv run -m fastmcp dev mcp_server.py ``` ### Installing as a Claude Desktop Tool ```bash # From activated environment fastmcp install mcp_server.py --name "Model Context Server" # Using uv directly uv run python -m fastmcp install mcp_server.py --name "Model Context Server" # With Datadog API key fastmcp install mcp_server.py --name "Model Context Server" -v DATADOG_API_KEY=your_api_key ``` ### Using the Tools The server provides the following tools: - `create_context` - Create a new context - `get_context` - Retrieve a specific context - `update_context` - Update an existing context - `delete_context` - Delete a context - `list_contexts` - List all contexts (with optional filtering) - `query_model` - Execute a query against a specific context - `health_check` - Server health check - `configure_datadog` - Configure Datadog integration at runtime ## Example Requests ### Creating a Context ```python result = await client.call_tool("create_context", { "context_id": "model-123", "model_name": "gpt-3.5", "data": { "parameters": { "temperature": 0.7 } }, "tags": ["production", "nlp"] }) ``` ### Executing a Query ```python result = await client.call_tool("query_model", { "context_id": "model-123", "query_data": { "prompt": "Hello, world!" } }) ``` ### Configuring Datadog ```python result = await client.call_tool("configure_datadog", { "api_key": "your_datadog_api_key", "app_key": "your_datadog_app_key", # Optional "site": "datadoghq.com" # Optional }) ``` ## Datadog Metrics The server reports the following metrics to Datadog: - `mcp.contexts.created` - Context creation events - `mcp.contexts.updated` - Context update events - `mcp.contexts.deleted` - Context deletion events - `mcp.contexts.accessed` - Context access events - `mcp.contexts.total` - Total number of contexts - `mcp.contexts.listed` - List contexts operation events - `mcp.queries.executed` - Query execution events - `mcp.server.startup` - Server startup events - `mcp.server.shutdown` - Server shutdown events ## Development See the included `mcp_example.py` for a client implementation example: ```bash # Run the example client (with activated environment) python mcp_example.py # Run with uv (no activation needed) uv run python mcp_example.py ``` ## License MIT ## Resources - [FastMCP Documentation](https://github.com/jlowin/fastmcp) - [Model Context Protocol](https://github.com/anthropics/model-context-protocol) specification - [uv Package Manager](https://github.com/astral-sh/uv) - [Datadog API Documentation](https://docs.datadoghq.com/api/)