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

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.

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).

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`.

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`.

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.

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