FastAPI Hello World Application

Name: FastAPI Hello World Application
Author: xxradar

@xxradar

リポジトリ

FastAPI Hello World Application について

A test repository created using the GitHub MCP server

#fastapi #api #gpt-integration

基本情報

カテゴリ

開発者ツール

ランタイム

python

トランスポート

stdio

公開者

xxradar

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"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

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

FAQ from FastAPI Hello World Application

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.

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.

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).

How can I access the API documentation?

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

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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