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MCP Server

@freedanfan

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

freedanfan

关于 MCP Server

基于 FastAPI 和 MCP(模型上下文协议),实现 AI 模型与开发环境 之间的标准化上下文交互,提升 AI 应用的可扩展性和可维护性。

MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-freedanfan": {
      "command": "python",
      "args": [
        "mcp_server.py"
      ]
    }
  }
}

MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Server?

MCP Server is a Python-based implementation of the Model Context Protocol (MCP) built on FastAPI. It enables standardized context interaction between AI models and development environments by providing efficient API endpoints, simplifying model deployment, and ensuring consistency in model input and output.

How to use MCP Server?

Clone the repository, install dependencies with pip install -r requirements.txt, then start the server with python mcp_server.py (defaults to 127.0.0.1:12000). Host and port can be customized via environment variables MCP_SERVER_HOST and MCP_SERVER_PORT. Run the included test client with python mcp_client.py.

Key features of MCP Server

  • JSON-RPC 2.0 request-response communication
  • Server-Sent Events (SSE) support for real-time notifications
  • Modular architecture for easy extension
  • Asynchronous processing with FastAPI
  • Complete test client implementation

Use cases of MCP Server

  • Integrating AI models with standardized context interaction
  • Building maintainable AI applications with unified input/output
  • Real-time communication between AI services and clients via SSE
  • Rapid prototyping of MCP-based services using the test client

常见问题

1

What does MCP Server do that other solutions don’t?

It provides a Python-based implementation of the Model Context Protocol, offering standardized JSON-RPC 2.0 endpoints, SSE connections, and a modular design that simplifies AI model deployment and integration.

2

What are the runtime requirements?

Python with dependencies listed in `requirements.txt`. The server runs as a standalone Python application using FastAPI.

3

Where does data live?

The server processes requests in‑memory and returns responses. No permanent storage is mentioned in the README.

4

How do I handle connection or SSE failures?

Ensure the server is running and the client uses the correct server URL. Check network connections and firewall settings for SSE connection issues.

5

How do I extend MCP Server?

Register new method handlers in the `MCPServer._register_methods` method, and optionally integrate actual AI models in the `handle_sample` method (e.g., by calling an external AI API).

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