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 .env file (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.

This server cannot be installed

-

security - not tested

F

license - not found

-

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

A Model Context Protocol server that bridges MCP clients with local LLM services, enabling seamless integration with MCP-compatible applications through standard tools like chat completion, model listing, and health checks.

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