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

1. 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

2. Install dependencies:

pip install -r requirements.txt

3. Run the server:

uvicorn main:app --reload

Production Deployment

1. 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

2. Run the server:

uvicorn main:app --host 0.0.0.0 --port 8000

Docker Deployment

1. Build the Docker image:

docker build -t mcp-server .

2. 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

1. In production:

   • Set DEBUG=false to disable API documentation
   • Use HTTPS
   • Configure proper authentication
   • Use secure database credentials
   • Set appropriate CORS policies

2. 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:
  • mistral
  • qwen
  • llama2

Installation

1. Clone the repository:

git clone <repository-url>
cd mcp-server

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Create a .env file:

cp .env.example .env

5. Update the .env file with your configuration:

ENVIRONMENT=development
OLLAMA_MODEL=mistral
OLLAMA_BASE_URL=http://localhost:11434

Running the Server

1. Start Ollama (if not already running):

ollama serve

2. Start the MCP server:

python main.py

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

1. Create a new provider class in the appropriate directory
2. Implement the required interface methods
3. Register the provider in the factory

Adding New Models

1. Add the model to the AVAILABLE_MODELS dictionary in model_providers/ollama.py
2. Update the model validation logic if needed

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.