FastAPI + FastMCP + Gemini Integration

A complete demonstration of integrating FastAPI with Google's Gemini AI through the Model Context Protocol (MCP) using FastMCP.

🎥 Demo Video

Watch the complete demonstration: Fast MCP.mp4

This video shows the full integration in action, including FastAPI startup, MCP tools testing, and Gemini AI interactions.

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Set Up Gemini API Key

Create a .env file in the project root:

GEMINI_API_KEY=your-gemini-api-key-here

Get your API key from Google AI Studio.

3. Start FastAPI

python start_fastapi.py

4. Test the Integration

# Test MCP tools directly
python test_mcp_cli.py

# Test Gemini integration
python gemini_integration.py

# Run complete demo
python demo.py

📁 Project Structure

FASTMCP/
├── main.py                 # FastAPI application
├── mcp_server.py          # FastMCP server with tools
├── gemini_integration.py  # Gemini SDK integration
├── test_mcp_cli.py        # CLI testing script
├── demo.py                # Complete demonstration
├── start_fastapi.py       # FastAPI startup script
├── requirements.txt       # Dependencies
└── README.md             # This file

🛠️ Core Components

FastAPI Application (main.py)

• RESTful API with user management (CRUD operations)

• Health check endpoint

• Auto-generated documentation at /docs

FastMCP Server (mcp_server.py)

Provides 7 MCP tools for API interaction:

• get_all_users() - Retrieve all users

• get_user_by_id(user_id) - Get specific user

• create_user(name, email, age) - Create new user

• update_user(user_id, name, email, age) - Update user

• delete_user(user_id) - Delete user

• get_health_status() - Check app health

• get_app_info() - Get app information

Gemini Integration (gemini_integration.py)

• Direct integration with Google's Gemini API

• Natural language interface for MCP tools

• Automatic tool selection based on prompts

🤖 How It Works

1. FastAPI provides a RESTful API for user management

2. FastMCP creates an MCP server that exposes API functions as tools

3. Gemini can call these tools automatically based on natural language prompts

Example Gemini Interactions

"Get all users from the FastAPI application"
→ Gemini calls get_all_users() and formats the response

"Create a new user named Alice with email alice@example.com and age 28"
→ Gemini calls create_user() with the specified parameters

"What is the health status of the application?"
→ Gemini calls get_health_status() and reports the status

🔧 API Endpoints

🧪 Testing

Test FastAPI Endpoints

# Get all users
python -c "import requests; print(requests.get('http://localhost:8000/users').json())"

# Health check
python -c "import requests; print(requests.get('http://localhost:8000/health').json())"

Test MCP Tools

python test_mcp_cli.py

Test Gemini Integration

python gemini_integration.py

🔑 Environment Variables

📚 Key Features

• ✅ Natural Language Interface - Ask questions in plain English

• ✅ Automatic Tool Selection - Gemini chooses appropriate MCP tools

• ✅ Real-time API Interaction - Direct communication with FastAPI

• ✅ Complete CRUD Operations - Full user management capabilities

• ✅ Error Handling - Comprehensive error management

• ✅ Cross-platform Support - Works on Windows, macOS, Linux

🐛 Troubleshooting

FastAPI Not Starting

• Check if port 8000 is available

• Ensure all dependencies are installed

• Run: uvicorn main:app --reload

MCP Tools Not Working

• Verify FastAPI is running on http://localhost:8000

• Check MCP server script for errors

Gemini Integration Issues

• Verify GEMINI_API_KEY is set correctly in .env file

• Check API quota and permissions

• Ensure google-genai package is installed

🔗 Learn More

• FastMCP Documentation

• Google Gemini SDK

• FastAPI Documentation

• MCP Protocol Specification

📄 License

This project is open source and available under the MIT License.