FastAPI Hello World Application
@xxradar
About FastAPI Hello World Application
A test repository created using the GitHub 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"
]
}
}
}Tools
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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
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.
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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