mcp-fastapi-learning
A test repository created using the GitHub MCP server
Documentation
FastAPI Hello World Application
A simple Hello World API built with FastAPI and MCP SSE support.
Features
- Root endpoint that returns a Hello World message
- Dynamic greeting endpoint that takes a name parameter
- OpenAI integration with GPT-4o for advanced AI-powered chat completions
- Automatic API documentation with Swagger UI
Prerequisites
- Python 3.7+ (for local setup)
- pip (Python package installer)
- OpenAI API key (for the `/openai` endpoint)
- Docker (optional, for containerized setup)
Setup Instructions
You can run this application either locally or using Docker.
Local Setup
1. Clone the repository
git clone https://github.com/xxradar/mcp-test-repo.git
cd mcp-test-repo2. Create a virtual environment (optional but recommended)
# On macOS/Linux
python -m venv venv
source venv/bin/activate
# On Windows
python -m venv venv
venv\Scripts\activate3. Install dependencies
pip install -r requirements.txt4. Run the application
uvicorn main:app --reloadThe application will start and be available at http://127.0.0.1:8000
Alternatively, you can run the application directly with Python:
python main.pyDocker Setup
1. Clone the repository
git clone https://github.com/xxradar/mcp-test-repo.git
cd mcp-test-repo2. Build the Docker image
docker build -t fastapi-hello-world .3. Run the Docker container
docker run -p 8000:8000 fastapi-hello-worldThe application will be available at http://localhost:8000
API Endpoints
- `GET /`: Returns a simple Hello World message
- `GET /hello/{name}`: Returns a personalized greeting with the provided name
- `GET /openai`: Returns a response from OpenAI's GPT-4o model (accepts an optional `prompt` query parameter)
- `GET /docs`: Swagger UI documentation
- `GET /redoc`: ReDoc documentation
OpenAI Integration
The `/openai` endpoint uses OpenAI's GPT-4o model and requires an OpenAI API key to be set as an environment variable:
Local Setup
# Set the OpenAI API key as an environment variable
export OPENAI_API_KEY=your_api_key_here
# Run the application
uvicorn main:app --reloadDocker Setup
# Run the Docker container with the OpenAI API key
docker run -p 8000:8000 -e OPENAI_API_KEY=your_api_key_here fastapi-hello-worldExample Usage
Using curl
# Get Hello World message
curl http://127.0.0.1:8000/
# Get personalized greeting
curl http://127.0.0.1:8000/hello/John
# Get OpenAI chat completion with default prompt
curl http://127.0.0.1:8000/openai
# Get OpenAI chat completion with custom prompt
curl "http://127.0.0.1:8000/openai?prompt=Tell%20me%20a%20joke%20about%20programming"Using MCP
Connect to MCP Inspector
npx @modelcontextprotocol/inspectorUsing a web browser
- Open http://127.0.0.1:8000/ in your browser for the Hello World message
- Open http://127.0.0.1:8000/hello/John in your browser for a personalized greeting
- Open http://127.0.0.1:8000/openai in your browser to get a response from OpenAI with the default prompt
- Open http://127.0.0.1:8000/openai?prompt=What%20is%20FastAPI? in your browser to get a response about FastAPI
- Open http://127.0.0.1:8000/docs for the Swagger UI documentation
Development
To make changes to the application, edit the `main.py` file. The server will automatically reload if you run it with the `--reload` flag.
Frequently asked questions
What is mcp-fastapi-learning?
mcp-fastapi-learning is A test repository created using the GitHub MCP server
How do I install mcp-fastapi-learning?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp-fastapi-learning open source?
Yes — it is hosted on GitHub at https://github.com/xxradar/mcp-test-repo.
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