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-test-repo": {
      "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?

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

Frequently asked questions

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.

Comments

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