Model Context Protocol Server
A FastAPI-based server that implements the Model Context Protocol to provide relevant information to AI models when processing user queries.
Environment Configuration
The server can be configured using environment variables. Create a .env file in the root directory with the following variables:
# Server Configuration
HOST=0.0.0.0 # Server host (0.0.0.0 for all interfaces)
PORT=8000 # Server port
ENVIRONMENT=development # Environment (development/production)
DEBUG=true # Enable debug mode
API_PREFIX=/api/v1 # API prefix for all endpoints
# Ollama Configuration
OLLAMA_BASE_URL=http://localhost:11434 # Ollama server URL
OLLAMA_MODEL=llama2 # Default model to use
# Database Configuration
DATABASE_URL=sqlite:///./catalog.db # Database connection URL
Deployment
Local Development
- Create and activate a virtual environment:
python -m venv .venv-py311
source .venv-py311/bin/activate # On Unix/macOS
# or
.venv-py311\Scripts\activate # On Windows
- Install dependencies:
pip install -r requirements.txt
- Run the server:
uvicorn main:app --reload
Production Deployment
- Set up environment variables for production:
HOST=0.0.0.0
PORT=8000
ENVIRONMENT=production
DEBUG=false
API_PREFIX=/api/v1
OLLAMA_BASE_URL=http://your-ollama-server:11434
OLLAMA_MODEL=llama2
- Run the server:
uvicorn main:app --host 0.0.0.0 --port 8000
Docker Deployment
- Build the Docker image:
docker build -t mcp-server .
- Run the container:
docker run -p 8000:8000 \
-e HOST=0.0.0.0 \
-e PORT=8000 \
-e ENVIRONMENT=production \
-e DEBUG=false \
-e OLLAMA_BASE_URL=http://your-ollama-server:11434 \
-e OLLAMA_MODEL=llama2 \
mcp-server
API Documentation
When running in development mode (DEBUG=true), API documentation is available at:
- Swagger UI:
http://your-server:8000/api/v1/docs - ReDoc:
http://your-server:8000/api/v1/redoc - OpenAPI JSON:
http://your-server:8000/api/v1/openapi.json
Security Considerations
- In production:
- Set DEBUG=false to disable API documentation
- Use HTTPS
- Configure proper authentication
- Use secure database credentials
- Set appropriate CORS policies
- For Ollama server:
- Ensure Ollama server is properly secured
- Use internal network for communication if possible
- Consider using API keys or other authentication methods
Monitoring and Logging
The server includes built-in logging with different levels based on the environment:
- Development: Debug level logging
- Production: Info level logging
Logs can be configured to output to files or external logging services.
Features
- Intelligent query routing based on query analysis
- Support for multiple data sources (Database, GraphQL, REST)
- Integration with Ollama models (Mistral, Qwen, Llama2)
- Environment-aware configuration (Development/Production)
- Comprehensive logging and error handling
- Health check endpoints
- Mock data support for development
Prerequisites
- Python 3.8+
- Ollama installed and running locally
- Required Ollama models:
Installation
- Clone the repository:
git clone <repository-url>
cd mcp-server
- Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
- Create a
.env file:
- Update the
.env file with your configuration:
ENVIRONMENT=development
OLLAMA_MODEL=mistral
OLLAMA_BASE_URL=http://localhost:11434
Running the Server
- Start Ollama (if not already running):
- Start the MCP server:
The server will be available at http://localhost:8000
API Endpoints
Get Context
curl -X POST http://localhost:8000/context \
-H "Content-Type: application/json" \
-d '{
"query": "Tell me about iPhone 15",
"model": "mistral"
}'
List Available Models
curl http://localhost:8000/models
Health Check
curl http://localhost:8000/health
Project Structure
mcp-server/
├── context_providers/ # Data source providers
│ ├── database.py # Database provider
│ ├── graphql.py # GraphQL provider
│ ├── rest.py # REST API provider
│ └── provider_factory.py
├── model_providers/ # AI model providers
│ ├── base.py # Base model provider
│ ├── ollama.py # Ollama integration
│ └── provider_factory.py
├── main.py # FastAPI application
├── query_analyzer.py # Query analysis logic
├── logger_config.py # Logging configuration
├── requirements.txt # Project dependencies
└── README.md # Project documentation
Development
Adding New Providers
- Create a new provider class in the appropriate directory
- Implement the required interface methods
- Register the provider in the factory
Adding New Models
- Add the model to the
AVAILABLE_MODELS dictionary in model_providers/ollama.py - Update the model validation logic if needed
Contributing
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.