About FastAPI Hello World Application
A sample API built with FastAPI, offering greeting endpoints and GPT-4o chat responses. It also provides Swagger UI documentation and supports MCP SSE.
Key features
- 01Hello World root endpoint
- 02Personalized name-based greeting
- 03GPT-4o chat response endpoint
- 04Swagger UI and ReDoc documentation
Use cases
- 01Check that a FastAPI service is running
- 02Return a greeting for a supplied name
- 03Request a GPT-4o response through an API
MCP support
The application supports MCP SSE and can be connected to using MCP Inspector.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEFastAPI 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
/openaiendpoint) - 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-repo
2. 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\activate
3. Install dependencies
pip install -r requirements.txt
4. Run the application
uvicorn main:app --reload
The application will start and be available at http://127.0.0.1:8000
Alternatively, you can run the application directly with Python:
python main.py
Docker Setup
1. Clone the repository
git clone https://github.com/xxradar/mcp-test-repo.git
cd mcp-test-repo
2. Build the Docker image
docker build -t fastapi-hello-world .
3. Run the Docker container
docker run -p 8000:8000 fastapi-hello-world
The application will be available at http://localhost:8000
API Endpoints
GET /: Returns a simple Hello World messageGET /hello/{name}: Returns a personalized greeting with the provided nameGET /openai: Returns a response from OpenAI's GPT-4o model (accepts an optionalpromptquery parameter)GET /docs: Swagger UI documentationGET /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 --reload
Docker 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-world
Example 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/inspector
Using 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.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.