iflow-mcp_zhouhaojiang-fastapi-with-fatmcp-agent 0.1.0

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FastMCP Integration Application Demo

中文说明 (Chinese Documentation)

This project demonstrates a modular application built with FastMCP, integrating an MCP server, a FastAPI interface, and LLM Agent capabilities.

Project Architecture

The project utilizes a modular design, separating different functionalities into distinct packages:

app/
├── __init__.py
├── api/                 # FastAPI Application Layer
│   ├── __init__.py
│   ├── main.py          # FastAPI main application entry point
│   └── routers/         # API route definitions
│       ├── __init__.py
│       ├── agent.py       # Agent mode routes
│       ├── mcp_resources.py # MCP resource routes
│       └── mcp_tools.py     # MCP tool routes
├── llm/                 # LLM (Large Language Model) Processing Layer
│   ├── __init__.py
│   ├── base.py          # Base LLM class
│   └── openai.py        # OpenAI implementation
└── mcp_server/          # MCP Server Definition Layer
    ├── __init__.py
    ├── base.py          # Base MCP Server class
    ├── run.py           # Script for internal Client connection or direct execution
    └── simple.py        # Simple MCP Server implementation (with tools & resources)
main.py                  # Main entry point (runs API or MCP server)

Core Workflow (API Mode):

1. Run MCP Server Independently: Start a separate MCP server process using python main.py --mode mcp, listening on a specified port (default: 8001) with SSE transport.
2. Run API Server: Start the FastAPI server using python main.py --mode api (listens on 8080).
3. Connection: The mcp_client within the FastAPI server connects to the independently running MCP server via SSE.
4. Request Handling: Frontend or other clients interact with the application through the HTTP API provided by FastAPI.
5. Tool/Resource/Agent Calls: FastAPI routes forward requests to the mcp_client (communicating with the MCP server) or the llm module (communicating with the LLM API).

Features

• Modular Design: Clear separation of concerns (API, LLM, MCP) for easy extension and maintenance.
• Dual Run Modes:
  • api mode: Runs the FastAPI server, requires a separately running MCP server.
  • mcp mode: Runs the MCP server directly for testing or connection by other clients.
• LLM Integration: Supports using OpenAI (or other extensible LLMs) to process tool outputs or execute in Agent mode.
• Agent Mode: Provides an /api/agent/process endpoint for the LLM to autonomously select and call MCP tools.
• Complete API: Offers RESTful endpoints for MCP tools, resources, and Agent functionality via FastAPI.
• Persistent Connection: The API server maintains a long-lived SSE connection to the MCP server for efficiency.

Installation

1. Clone the repository (if needed).

2. Install dependencies (using uv or pip):

   # Recommended: use uv
   uv pip install -e .
   
   # Alternatively, use pip
   # pip install -e .
   

3. Set Environment Variables:

   • The OPENAI_API_KEY environment variable is required to use LLM features.
   • You can create a .env file in the project root and define it there:

     OPENAI_API_KEY=sk-...
     

Usage

Running the API Server (Recommended)

This mode requires first starting the standalone MCP server.

1. Start the MCP Server (in one terminal):

   # Uses SSE transport, listening on 127.0.0.1:8001 (default)
   python main.py --mode mcp
   
   # Or specify a different host and port
   # python main.py --mode mcp --mcp-host 0.0.0.0 --mcp-port 8002
   

2. Start the API Server (in another terminal):

   # Listens on 0.0.0.0:8080 (default)
   python main.py --mode api
   
   # Use a different port or enable hot-reloading (for development)
   # python main.py --mode api --port 9000 --reload
   

   The API server is now accessible at http://localhost:8080 (or your specified port). It will automatically connect to the MCP server started in step 1 (default connection: http://localhost:8001/sse).

Running the MCP Server Directly

If you only need to run the MCP server (e.g., for direct connection by other FastMCP clients):

# Default: Use SSE transport, listening on 127.0.0.1:8001
python main.py --mode mcp

# Use Standard I/O (stdio) transport
# python main.py --mode mcp --mcp-transport stdio

API Endpoints (Accessed via API Server)

The API server runs on http://localhost:8080 (default).

• MCP Tools (/api/tools):
  • GET /: List all available tools and their parameters.
  • POST /{tool_name}: Call a specific tool. Example request body: {"params": {"a": 5, "b": 3}, "use_llm": true, "system_message": "Explain the result"}
• MCP Resources (/api/resources):
  • GET /: List all available resource URIs and their types.
  • GET /{resource_path:path}: Get the content of a specific resource (e.g., GET /api/resources/example/greeting).
• Agent Mode (/api/agent):
  • POST /process: Let the LLM autonomously handle a user request, potentially calling tools. Example request body: {"prompt": "What is 5 plus 3?"}
• Health Checks:
  • GET /health: Check if the API server is running.
  • GET /api/tools/health: Check the connection status between the API server and the MCP server.

Extending the Application

Adding New Tools

1. Extend BaseMCPServer in app/mcp_server/simple.py (or create a new server file).
2. Define new tools using the @self.mcp.tool() decorator within the _register_tools method.
3. If you created a new server file, import and instantiate it in app/mcp_server/run.py.

Adding New LLM Providers

1. Create a new Python file in the app/llm/ directory (e.g., anthropic.py).
2. Create a new class inheriting from app.llm.base.BaseLLM.
3. Implement the generate and generate_with_tools methods.
4. Import and use the new LLM class where needed (e.g., in agent.py).

Development

Enable hot-reloading when running the API server for development:

# Ensure the standalone MCP server is still running
python main.py --mode api --reload