Production Deployment

Architecture Patterns

Single Server

Client → MCP Server
Simple, suitable for low traffic

Load Balanced

Client → Load Balancer → [Server 1, Server 2, Server 3]
Distributes load, handles traffic spikes

Multi-Region

Region A: [Server 1, Server 2]
Region B: [Server 3, Server 4]

Low latency for each region
High availability

Container Deployment

FROM python:3.11
WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY server.py .

EXPOSE 8000
CMD ["python", "server.py"]

docker build -t mcp-server .
docker run -p 8000:8000 mcp-server

Monitoring

Track server health:

@server.health_check()
def health():
    return {
        "status": "healthy",
        "timestamp": now(),
        "uptime_seconds": uptime(),
        "tool_calls_total": stats.total_calls,
        "tool_calls_per_minute": stats.calls_per_minute
    }

Metrics

Track:
- Tool call success rate
- Average latency per tool
- Error rate
- Cache hit rate
- Authentication failures

Graceful Shutdown

Handle termination safely:

def shutdown_handler():
    # Complete in-flight requests
    wait_for_requests(timeout=30)
    
    # Close database connections
    db.close()
    
    # Log shutdown
    logger.info("Server shutting down")
    
    exit(0)

signal.signal(signal.SIGTERM, shutdown_handler)

High Availability

Strategies:
1. Multiple servers in cluster
2. Health checks (remove unhealthy)
3. Automatic failover
4. Shared database (stateless servers)
5. Load balancer with automatic scaling

Cost Optimization

Reduce costs:
- Use smaller instances for light load
- Auto-scale based on demand
- Cache frequent results
- Batch tool calls when possible
- Close idle connections