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FastAPI Hello World Application MCP Server

@xxradar

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基本情報

カテゴリ

開発者ツール

トランスポート

stdio

作者

xxradar

FastAPI Hello World Application MCP Server について

A test repository created using the GitHub MCP server

#fastapi#api#gpt-integration

FastAPI Hello World Application MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-test-repo": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

FastAPI Hello World Application MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is FastAPI Hello World Application?

It is a simple Hello World API built with FastAPI and MCP SSE support. It provides basic greeting endpoints and an endpoint for AI‑powered chat completions using OpenAI GPT‑4o.

How to use FastAPI Hello World Application?

Clone the repository, install dependencies, and set the OPENAI_API_KEY environment variable if using the OpenAI endpoint. Run locally with uvicorn main:app --reload or python main.py, or use Docker with docker build -t fastapi-hello-world . then docker run -p 8000:8000 -e OPENAI_API_KEY=your_key fastapi-hello-world. The MCP server can be connected via the MCP Inspector.

Key features of FastAPI Hello World Application

  • Root endpoint returning a Hello World message
  • Dynamic greeting endpoint with a name parameter
  • OpenAI GPT‑4o chat completion endpoint
  • Automatic Swagger UI and ReDoc documentation
  • Supports local and Docker deployment
  • MCP SSE support for integration

Use cases of FastAPI Hello World Application

  • Quick demonstration of a FastAPI‑based MCP server
  • Testing MCP SSE protocol with a simple API
  • Experimenting with OpenAI GPT‑4o via HTTP endpoint
  • Learning FastAPI with MCP integration

よくある質問

1

What dependencies are required?

Python 3.7+, pip, and an OpenAI API key (only for the `/openai` endpoint). Docker is optional.

2

How do I run the application?

Locally with `uvicorn main:app --reload` or `python main.py`. Docker: build the image and run the container with port `8000` mapped and the `OPENAI_API_KEY` environment variable.

3

Where does the data live?

The application does not persist any data; it only returns responses generated by its own endpoints or the OpenAI API.

4

What transports and authentication are used?

The MCP server uses SSE as its transport. The OpenAI endpoint requires the `OPENAI_API_KEY` to be set as an environment variable; no other authentication is needed.

5

What are the known limits?

The `/openai` endpoint will fail if the `OPENAI_API_KEY` environment variable is not set. No other limitations are documented.

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