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
-
Clone the repository:
git clone https://github.com/yourusername/fastmcp.git cd fastmcp -
Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate -
Install dependencies:
pip install -r requirements.txt -
Create a
.envfile (copy from.env.mcp) and configure:# 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
python run_server.py
Option 2: Direct execution
python mcp_server.py
Option 3: With custom configuration
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:
{
"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:
npx @modelcontextprotocol/inspector python mcp_server.py
Available Tools
1. Chat Completion
Send messages to your LLM service:
{
"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:
{
"name": "list_models",
"arguments": {}
}
3. Health Check
Check if your LLM service is running:
{
"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
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.