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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-fastapi-learning": {
      "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?

A simple Hello World API built with FastAPI and MCP SSE support. It provides basic greeting endpoints, integrates with OpenAI’s GPT-4o for AI-powered chat completions, and includes automatic API documentation via Swagger UI and ReDoc.

How to use FastAPI Hello World Application?

Clone the repository, create a Python virtual environment, install dependencies from requirements.txt, then run with uvicorn main:app --reload or python main.py. Alternatively, build a Docker image and run the container on port 8000. Access endpoints via curl or browser, or connect to the MCP Inspector using npx @modelcontextprotocol/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 integration for advanced chat completions
  • Automatic API documentation (Swagger UI and ReDoc)
  • MCP SSE support for Model Context Protocol
  • Optional Docker containerized deployment

Use cases of FastAPI Hello World Application

  • Quickly verify a FastAPI setup with a hello world response
  • Generate personalized greetings via the /hello/{name} endpoint
  • Test OpenAI chat completions with a custom prompt
  • Explore automatic API documentation for development and testing

よくある質問

1

What prerequisites are needed to run the application?

Python 3.7+ and pip are required for local setup. For the `/openai` endpoint, an OpenAI API key must be set as an environment variable. Docker is optional for containerized deployment.

2

How do I set the OpenAI API key?

Export the key as the `OPENAI_API_KEY` environment variable before running the application locally (`export OPENAI_API_KEY=your_key_here`). For Docker, pass it using `-e OPENAI_API_KEY=your_key_here` when running the container.

3

What endpoints does the application expose?

`GET /` (hello world), `GET /hello/{name}` (personalized greeting), `GET /openai` (chat completion with optional `prompt` parameter), `GET /docs` (Swagger UI), and `GET /redoc` (ReDoc documentation).

4

How can I access the API documentation?

Open `/docs` in your browser for Swagger UI, or `/redoc` for ReDoc.

5

How do I use the MCP SSE support?

Start the server and connect using the MCP Inspector by running `npx @modelcontextprotocol/inspector` in your terminal.

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