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FastAPI Hello World Application

@xxradar

代码仓库

关于 FastAPI Hello World Application

A test repository created using the GitHub MCP server

#fastapi#api#gpt-integration

基本信息

分类

开发工具

运行时

python

传输方式

stdio

发布者

xxradar

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-fastapi-learning": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

工具

未检测到工具

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 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.

评论

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