FastMCP

# FastMCP - Model Context Protocol Server FastMCP is a Model Context Protocol (MCP) server that provides LLM services through the MCP standard. It acts as a bridge between MCP clients and your local LLM service, enabling seamless integration with MCP-compatible applications. ## Features - 🚀 **MCP Protocol Compliance**: Full implementation of Model Context Protocol - 🔧 **Tools**: Chat completion, model listing, health checks - 📝 **Prompts**: Pre-built prompts for common tasks (assistant, code review, summarization) - 📊 **Resources**: Server configuration and LLM service status - 🔄 **Streaming Support**: Both streaming and non-streaming responses - 🔒 **Configurable**: Environment-based configuration - 🛡️ **Robust**: Built-in error handling and health monitoring - 🔌 **Integration Ready**: Works with any OpenAI-compatible LLM service ## Getting Started ### Prerequisites - Python 3.9+ - pip - Local LLM service running on port 5001 (OpenAI-compatible API) - MCP client (e.g., Claude Desktop, MCP Inspector) ### Installation 1. Clone the repository: ```bash git clone https://github.com/yourusername/fastmcp.git cd fastmcp ``` 2. Create a virtual environment and activate it: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` 4. Create a `.env` file (copy from `.env.mcp`) and configure: ```env # Server Settings MCP_SERVER_NAME=fastmcp-llm-router MCP_SERVER_VERSION=0.1.0 # LLM Service Configuration LOCAL_LLM_SERVICE_URL=http://localhost:5001 # Optional: API Key for LLM service # LLM_SERVICE_API_KEY=your_api_key_here # Timeouts (in seconds) LLM_REQUEST_TIMEOUT=60 HEALTH_CHECK_TIMEOUT=10 # Logging LOG_LEVEL=INFO ``` ### Running the MCP Server #### Option 1: Using the CLI script ```bash python run_server.py ``` #### Option 2: Direct execution ```bash python mcp_server.py ``` #### Option 3: With custom configuration ```bash python run_server.py --llm-url http://localhost:5001 --log-level DEBUG ``` The MCP server will run on stdio and can be connected to by MCP clients. ## MCP Client Integration ### Claude Desktop Integration Add to your Claude Desktop configuration: ```json { "mcpServers": { "fastmcp-llm-router": { "command": "python", "args": ["/path/to/fastmcp/mcp_server.py"], "env": { "LOCAL_LLM_SERVICE_URL": "http://localhost:5001" } } } } ``` ### MCP Inspector Test your server with MCP Inspector: ```bash npx @modelcontextprotocol/inspector python mcp_server.py ``` ## Available Tools ### 1. Chat Completion Send messages to your LLM service: ```json { "name": "chat_completion", "arguments": { "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"} ], "model": "default", "temperature": 0.7 } } ``` ### 2. List Models Get available models from your LLM service: ```json { "name": "list_models", "arguments": {} } ``` ### 3. Health Check Check if your LLM service is running: ```json { "name": "health_check", "arguments": {} } ``` ## Available Prompts - **chat_assistant**: General AI assistant prompt - **code_review**: Code review and analysis - **summarize**: Text summarization ## Available Resources - **config://server**: Server configuration - **status://llm-service**: LLM service status ## Project Structure ``` fastmcp/ ├── app/ │ ├── api/ │ │ └── v1/ │ │ └── api.py # API routes │ ├── core/ │ │ └── config.py # Application configuration │ ├── models/ # Database models │ ├── services/ # Business logic │ └── utils/ # Utility functions ├── tests/ # Test files ├── .env.example # Example environment variables ├── requirements.txt # Project dependencies └── README.md # This file ``` ## Contributing 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.