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FastMCP Integration Application Demo MCP Server

@ZhouhaoJiang

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

ZhouhaoJiang

关于 FastMCP Integration Application Demo MCP Server

A modular application API interface based on FastMCP, integrating a demo of the MCP server, FastAPI interface and LLM Agent processing capabilities | 一个基于FastMCP的模块化应用，集成了MCP服务器、FastAPI接口和LLM Agent 处理能力的 demo

#fastapi#mcp#llm-agent

FastMCP Integration Application Demo 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": {
    "fastapi-with-fatmcp-agent": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-e",
        "."
      ]
    }
  }
}

FastMCP Integration Application Demo 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 FastMCP Integration Application Demo?

This project demonstrates a modular application built with FastMCP, integrating an MCP server, a FastAPI interface, and LLM Agent capabilities. It is a demo for developers exploring FastMCP-based architectures.

How to use FastMCP Integration Application Demo?

Install dependencies (uv/pip recommended) and set the OPENAI_API_KEY environment variable. First start the MCP server with python main.py --mode mcp, then start the API server with python main.py --mode api. The API server connects to the MCP server via SSE and provides HTTP endpoints.

Key features of FastMCP Integration Application Demo

  • Modular design with clear separation of concerns
  • Dual run modes: API server and standalone MCP server
  • LLM integration with OpenAI (extensible)
  • Agent mode for autonomous tool selection by the LLM
  • Complete RESTful API for tools, resources, and agent
  • Persistent SSE connection between servers

Use cases of FastMCP Integration Application Demo

  • Building a FastMCP-based application with an HTTP API layer
  • Enabling an LLM agent to autonomously call MCP tools
  • Testing and debugging MCP server interactions via REST endpoints
  • Extending the platform with custom tools and LLM providers

常见问题

1

What are the two run modes and how do they differ?

`api` mode runs the FastAPI server and requires a separately running MCP server. `mcp` mode runs the MCP server directly for testing or connection by other clients.

2

Which LLM providers are supported?

OpenAI is supported out of the box. The `BaseLLM` class can be extended to add other providers (e.g., Anthropic).

3

How can I add new tools?

Extend `BaseMCPServer` and define new tools using the `@self.mcp.tool()` decorator in the `_register_tools` method. Then import and instantiate the server in `app/mcp_server/run.py`.

4

What transport does the MCP server use by default?

The default transport is SSE. You can switch to Standard I/O (stdio) with `--mcp-transport stdio`.

5

Is there a way to monitor server health?

Yes, the API server provides `/health` for its own status and `/api/tools/health` for the connection status between the API server and the MCP server.

评论

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