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FastAPI Hello World Application

@xxradar

Repository

About FastAPI Hello World Application

A test repository created using the GitHub MCP server

#fastapi#api#gpt-integration

Basic information

Category

Developer Tools

Runtime

python

Transports

stdio

Publisher

xxradar

Config

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

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

Tools

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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?

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

FAQ from FastAPI Hello World Application

What dependencies are required?

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

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.

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.

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.

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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