FastAPI Hello World Application MCP Server

@xxradar

Repository

Basic information

About FastAPI Hello World Application MCP Server

A test repository created using the GitHub MCP server

#fastapi #api #gpt-integration

FastAPI Hello World Application MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

FastAPI Hello World Application MCP Server tools

Tool list not available yet

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

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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