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Management Guide

Lifecycle Management for MCP Servers

Effective management ensures MCP servers remain reliable, secure, and performant throughout their lifecycle.

Management Phases

1. Planning

  • Requirements gathering
  • Architecture design
  • Resource allocation
  • Risk assessment

2. Development

  • Implementation
  • Testing
  • Documentation
  • Code review

3. Deployment

  • Environment setup
  • Configuration management
  • Release process
  • Rollout strategy

4. Operation

  • Monitoring
  • Maintenance
  • Support
  • Optimization

5. Evolution

  • Updates and patches
  • Feature additions
  • Performance tuning
  • Security hardening

Configuration Management

Environment-Based Configuration

# config/production.yaml
server:
  port: 8000
  host: 0.0.0.0
  workers: 4

database:
  host: prod-db.example.com
  pool_size: 20
  
monitoring:
  enabled: true
  level: info
  
rate_limiting:
  enabled: true
  max_requests: 100
  window: 60

Dynamic Configuration

class ConfigManager:
    def __init__(self):
        self.config = self.load_config()
        self.watch_changes()
    
    def load_config(self):
        env = os.getenv('MCP_ENV', 'development')
        return load_yaml(f'config/{env}.yaml')
    
    def watch_changes(self):
        # Watch for config file changes
        observer = Observer()
        observer.schedule(ConfigHandler(self), 'config/')
        observer.start()

Version Management

Semantic Versioning

MAJOR.MINOR.PATCH

1.0.0 - Initial release
1.0.1 - Bug fix
1.1.0 - New feature (backward compatible)
2.0.0 - Breaking change

API Versioning

@mcp.tool(version="1.0")
def old_tool(param: str) -> str:
    """Deprecated version"""
    return process_v1(param)

@mcp.tool(version="2.0")
def new_tool(param: str, options: dict = None) -> dict:
    """Current version with enhanced features"""
    return process_v2(param, options)

Dependency Management

Python Dependencies

# pyproject.toml
[project]
dependencies = [
    "mcp>=1.0,<2.0",  # Pin major version
    "pydantic~=2.5",   # Compatible releases
    "requests==2.31.0", # Exact version
]

[tool.pip-tools]
generate-hashes = true
resolver = "backtracking"

Dependency Updates

# Check for updates
pip list --outdated

# Update dependencies
pip-compile --upgrade pyproject.toml

# Security audit
pip-audit

Health Monitoring

Health Check Endpoint

@app.route('/health')
def health_check():
    checks = {
        'server': 'healthy',
        'database': check_database(),
        'dependencies': check_dependencies(),
        'disk_space': check_disk_space(),
        'memory': check_memory()
    }
    
    status = 'healthy' if all(
        v == 'healthy' for v in checks.values()
    ) else 'unhealthy'
    
    return {
        'status': status,
        'timestamp': datetime.utcnow().isoformat(),
        'checks': checks
    }

Metrics Collection

from prometheus_client import Counter, Histogram, Gauge

# Define metrics
request_count = Counter('mcp_requests_total', 'Total requests')
request_duration = Histogram('mcp_request_duration_seconds', 'Request duration')
active_connections = Gauge('mcp_active_connections', 'Active connections')

# Collect metrics
@measure_time(request_duration)
def handle_request(request):
    request_count.inc()
    with active_connections.track_inprogress():
        return process_request(request)

Logging Strategy

Structured Logging

import structlog

logger = structlog.get_logger()

def process_tool(tool_name: str, params: dict):
    logger.info(
        "tool_execution_started",
        tool=tool_name,
        params=params,
        timestamp=datetime.utcnow().isoformat()
    )
    
    try:
        result = execute_tool(tool_name, params)
        logger.info(
            "tool_execution_completed",
            tool=tool_name,
            duration_ms=elapsed_time
        )
        return result
    except Exception as e:
        logger.error(
            "tool_execution_failed",
            tool=tool_name,
            error=str(e),
            traceback=traceback.format_exc()
        )
        raise

Log Aggregation

# filebeat.yml
filebeat.inputs:
- type: log
  enabled: true
  paths:
    - /var/log/mcp-server/*.log
  json.keys_under_root: true
  json.add_error_key: true

output.elasticsearch:
  hosts: ["elasticsearch:9200"]
  index: "mcp-logs-%{+yyyy.MM.dd}"

Backup and Recovery

Backup Strategy

class BackupManager:
    def __init__(self):
        self.backup_dir = '/backups'
        self.retention_days = 30
    
    def backup_state(self):
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        backup_path = f"{self.backup_dir}/backup_{timestamp}.tar.gz"
        
        # Backup configuration
        self.backup_config(backup_path)
        
        # Backup data
        self.backup_data(backup_path)
        
        # Clean old backups
        self.clean_old_backups()
        
        return backup_path

Disaster Recovery

#!/bin/bash
# disaster_recovery.sh

# Stop current server
systemctl stop mcp-server

# Restore from backup
tar -xzf /backups/latest.tar.gz -C /

# Restore database
psql < /backups/database.sql

# Start server
systemctl start mcp-server

# Verify health
curl http://localhost:8000/health

Change Management

Change Process

  1. Request - Document change request
  2. Review - Technical and business review
  3. Approval - Get necessary approvals
  4. Testing - Test in staging environment
  5. Implementation - Deploy to production
  6. Verification - Verify successful deployment
  7. Documentation - Update documentation

Rollback Plan

class DeploymentManager:
    def deploy(self, version: str):
        # Save current version
        self.save_rollback_point()
        
        try:
            # Deploy new version
            self.update_code(version)
            self.run_migrations()
            self.restart_services()
            
            # Verify deployment
            if not self.verify_health():
                raise DeploymentError("Health check failed")
                
        except Exception as e:
            logger.error(f"Deployment failed: {e}")
            self.rollback()
            raise
    
    def rollback(self):
        logger.info("Starting rollback")
        self.restore_previous_version()
        self.restart_services()
        self.verify_health()

Capacity Planning

Resource Monitoring

def monitor_resources():
    return {
        'cpu_percent': psutil.cpu_percent(interval=1),
        'memory_percent': psutil.virtual_memory().percent,
        'disk_usage': psutil.disk_usage('/').percent,
        'network_connections': len(psutil.net_connections()),
        'thread_count': threading.active_count()
    }

Scaling Strategy

# kubernetes/hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: mcp-server-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: mcp-server
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

Documentation Management

Documentation Standards

  • README.md - Quick start and overview
  • API.md - Tool and resource documentation
  • CONFIGURATION.md - Configuration options
  • TROUBLESHOOTING.md - Common issues and solutions
  • CHANGELOG.md - Version history

Documentation Generation

def generate_docs():
    """Generate documentation from code"""
    docs = {
        'tools': extract_tool_docs(),
        'resources': extract_resource_docs(),
        'configuration': extract_config_schema(),
        'api_version': MCP_VERSION
    }
    
    render_markdown(docs, 'docs/API.md')

Next Steps