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:
- mistral
- qwen
- llama2

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:

cp .env.example .env

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):

ollama serve

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

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.