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2025-04-14

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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-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 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 --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

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.

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    Reviews

    2 (1)
    Avatar
    user_cUcMWiS6
    2025-04-15

    As a dedicated MCP user, I highly recommend the Linear Regression MCP created by HeetVekariya. This tool is exceptionally user-friendly and provides precise linear regression modeling. It’s perfect for both beginners and advanced users needing reliable predictions and analysis. The seamless integration and thorough documentation make it a top choice in the MCP ecosystem. Check it out here: https://mcp.so/server/Linear-Regression-MCP/HeetVekariya