MedX MCP Server

# Deployment Guide for MCP Server Mock Jivi ## Prerequisites - Python 3.13+ (or Docker) - OpenAI API key - Environment variables configured ## Deployment Options ### Option 1: Docker (Recommended for Production) #### Build and Run Locally ```bash # Build image docker build -t mcp-server-mock-jivi . # Run container docker run -d \ -p 8000:8000 \ -e OPENAI_API_KEY=your-key-here \ -e MCP_SERVER_TOKEN=your-secret-token \ --name mcp-server \ mcp-server-mock-jivi ``` #### Using Docker Compose ```bash # Create .env file cat > .env << EOF OPENAI_API_KEY=your-key-here MCP_SERVER_TOKEN=your-secret-token OPENAI_MODEL=gpt-4o-mini LOG_LEVEL=INFO EOF # Start services docker-compose up -d # View logs docker-compose logs -f # Stop services docker-compose down ``` ### Option 2: Direct Python (Development/Local) ```bash # Install dependencies pip install -r requirements.txt # Set environment variables export OPENAI_API_KEY=your-key-here export MCP_SERVER_TOKEN=your-secret-token # Run server python main.py ``` ### Option 3: Cloud Platforms #### Railway 1. Install Railway CLI: `npm i -g @railway/cli` 2. Login: `railway login` 3. Initialize: `railway init` 4. Add environment variables in Railway dashboard 5. Deploy: `railway up` #### Render 1. Connect GitHub repository 2. Create new Web Service 3. Set: - Build Command: `pip install -r requirements.txt` - Start Command: `python main.py` 4. Add environment variables 5. Deploy #### Heroku ```bash # Install Heroku CLI, then: heroku create your-app-name heroku config:set OPENAI_API_KEY=your-key heroku config:set MCP_SERVER_TOKEN=your-token git push heroku main ``` #### AWS (ECS/Fargate) 1. Push Docker image to ECR 2. Create ECS task definition with environment variables 3. Deploy to Fargate or EC2 #### Google Cloud Run ```bash gcloud builds submit --tag gcr.io/PROJECT-ID/mcp-server gcloud run deploy mcp-server \ --image gcr.io/PROJECT-ID/mcp-server \ --platform managed \ --region us-central1 \ --set-env-vars OPENAI_API_KEY=your-key ``` #### Azure Container Instances ```bash az container create \ --resource-group myResourceGroup \ --name mcp-server \ --image mcp-server-mock-jivi \ --dns-name-label mcp-server \ --ports 8000 \ --environment-variables OPENAI_API_KEY=your-key ``` ### Option 4: VPS/Server (Ubuntu/Debian) ```bash # SSH into server ssh user@your-server # Install Python and dependencies sudo apt update sudo apt install python3.13 python3-pip -y # Clone repository git clone your-repo-url cd mcp_server_mock_jivi # Create virtual environment python3 -m venv venv source venv/bin/activate # Install dependencies pip install -r requirements.txt # Set up systemd service sudo nano /etc/systemd/system/mcp-server.service ``` Systemd service file (`/etc/systemd/system/mcp-server.service`): ```ini [Unit] Description=MCP Server Mock Jivi After=network.target [Service] Type=simple User=www-data WorkingDirectory=/path/to/mcp_server_mock_jivi Environment="OPENAI_API_KEY=your-key" Environment="MCP_SERVER_TOKEN=your-token" Environment="PATH=/path/to/mcp_server_mock_jivi/venv/bin" ExecStart=/path/to/mcp_server_mock_jivi/venv/bin/python main.py Restart=always [Install] WantedBy=multi-user.target ``` ```bash # Enable and start service sudo systemctl enable mcp-server sudo systemctl start mcp-server sudo systemctl status mcp-server ``` ## Environment Variables Required: - `OPENAI_API_KEY` - Your OpenAI API key Optional: - `MCP_SERVER_TOKEN` - Auth token (default: "super-secret-token") - `OPENAI_MODEL` - Model name (default: "gpt-4o-mini") - `LOG_LEVEL` - Logging level (default: "INFO") - `LOG_FILE` - Log file path (default: "server.log") - `HOST` - Bind host (default: "0.0.0.0") - `PORT` - Port number (default: 8000) ## Production Considerations ### Security - ✅ Use HTTPS (nginx/Traefik reverse proxy) - ✅ Set strong `MCP_SERVER_TOKEN` - ✅ Restrict firewall rules - ✅ Use secrets management (AWS Secrets Manager, etc.) ### Performance - Use `gunicorn` with uvicorn workers for production - Set `workers = (2 * CPU cores) + 1` - Enable connection pooling ### Monitoring - Health check: `GET /healthz` - Ready check: `GET /readyz` - Monitor logs: `tail -f server.log` - Set up alerts for 503 responses ### Reverse Proxy (nginx example) ```nginx server { listen 80; server_name your-domain.com; location / { proxy_pass http://localhost:8000; proxy_http_version 1.1; proxy_set_header Upgrade $http_upgrade; proxy_set_header Connection 'upgrade'; proxy_set_header Host $host; proxy_cache_bypass $http_upgrade; proxy_read_timeout 300s; proxy_send_timeout 300s; } } ``` ## Verification After deployment, verify: ```bash # Health check curl http://your-server:8000/healthz # Ready check curl http://your-server:8000/readyz # Manifest (requires auth) curl -H "Authorization: Bearer your-token" \ http://your-server:8000/mcp/manifest ```