Taking Your MCP Server to Production

Complete workflow for deploying your MCP server to production, including Kubernetes deployment, monitoring, and scaling.

This document outlines the comprehensive lifecycle for developing, testing, and deploying Model Context Protocol (MCP) servers. Understanding this end-to-end journey helps developers coordinate effectively throughout the development process.

Complete MCP Development Lifecycle

The MCP development lifecycle follows a three-phase approach that ensures reliable, secure, and high-performing deployments:

Phase 1: Local Development and Testing

This phase focuses on rapid development and iteration in a controlled local environment.

Core Development Activities

1. MCP Server Development

   • Implement MCP servers using FastMCP or FastAPI patterns
   • Configure authentication integration
   • Add comprehensive error handling and logging
   • Create reusable tools and resources

2. Local Testing

   • Test with Cursor agents for rapid feedback
   • Test with Claude Desktop for validation
   • Validate functionality in development environment

3. Quality Assurance

   • Pre-commit hooks for code quality
   • Unit tests for tool functionality
   • Documentation validation
   • Security compliance (credential management, input validation)

Development Environment

Required Setup:

• Python development environment with FastMCP or FastAPI
• MCP client tools (Cursor, Claude Desktop)
• Testing framework (pytest)

Key Technologies:

• FastAPI for enterprise-scale MCP servers with async/await
• MCP Protocol for standardized AI agent integration
• pytest for comprehensive testing

Phase 2: Pre-Production Testing

Testing Checklist

Before moving to production:

• Functionality: All tools tested and working correctly
• Documentation: Complete setup, usage, and troubleshooting docs
• Environment Config: Ready for deployment (configs, dependencies)
• Security: Authentication tested, credentials properly managed
• Quality: Code quality checks passing, pre-commit hooks configured
• Testing: Both integration and unit tests completed

Phase 3: Production Deployment

Kubernetes Deployment

The MCP server template includes Kubernetes deployment configurations:

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: mcp-server
        image: "your-registry/mcp-server:latest"
        resources:
          requests:
            memory: "256Mi"
            cpu: "100m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3000
        readinessProbe:
          httpGet:
            path: /health
            port: 3000

Production Features

• High availability: Multi-replica deployment
• Resource management: CPU/memory limits and requests
• Health checks: Liveness and readiness probes
• Security: Pod security contexts and service accounts
• Networking: Services and ingress configuration

Monitoring & Observability

Implement comprehensive monitoring:

• Health endpoints: /health for Kubernetes probes
• Metrics: Expose Prometheus-compatible metrics
• Logging: Structured logging for debugging
• Tracing: Request tracing for performance analysis

Deployment Workflow

1. Build container image

   podman build -t your-registry/mcp-server:v1.0 .
   podman push your-registry/mcp-server:v1.0
   

2. Apply Kubernetes manifests

   kubectl apply -f kubernetes/namespace.yaml
   kubectl apply -f kubernetes/configmap.yaml
   kubectl apply -f kubernetes/secret.yaml
   kubectl apply -f kubernetes/deployment.yaml
   kubectl apply -f kubernetes/service.yaml
   

3. Verify deployment

   kubectl get pods -n mcp-servers
   kubectl logs -f deployment/mcp-server -n mcp-servers
   

Next Steps

For New Developers

1. Start with the Quick Start guide
2. Review Best Practices for production-ready code
3. Test locally before deployment

For Production Deployment

1. Prepare Kubernetes cluster
2. Configure secrets and environment variables
3. Deploy using provided manifests
4. Monitor and scale as needed

For questions or support, visit GitHub Discussions.