Cortex Resource Manager

# MCP Lifecycle Management Implementation Summary ## Overview Successfully implemented comprehensive MCP (Model Context Protocol) server lifecycle management tools for Kubernetes-based deployments. This implementation provides a complete API for managing MCP servers running in Kubernetes clusters. ## Implementation Details ### Core Module: `src/resource_manager_mcp_server/__init__.py` (611 lines) #### Main Class: MCPLifecycleManager A robust manager class that handles all lifecycle operations for MCP servers in Kubernetes. **Key Features:** - In-cluster and kubeconfig-based authentication - Namespace isolation - Comprehensive error handling - Input validation - Automatic health checking - Status monitoring #### Implemented Functions ##### 1. list_mcp_servers() **Purpose:** List all registered MCP servers in the cluster **Features:** - Label-based filtering (default: `app.kubernetes.io/component=mcp-server`) - Returns comprehensive server information - Includes service endpoints - Real-time status detection **Returns:** ```python [ { "name": "server-name", "status": "running|stopped|scaling|pending", "replicas": 3, "ready_replicas": 3, "endpoints": ["http://10.0.0.1:8080"] } ] ``` ##### 2. get_mcp_status(name) **Purpose:** Get detailed status of a specific MCP server **Features:** - Comprehensive deployment status - Replica information (ready, available, updated) - Service endpoint discovery - Last activity timestamp - Kubernetes condition details **Returns:** ```python { "name": "server-name", "status": "running", "replicas": 3, "ready_replicas": 3, "available_replicas": 3, "updated_replicas": 3, "endpoints": ["http://10.0.0.1:8080"], "last_activity": "2025-12-08T19:00:00Z", "conditions": [...] } ``` ##### 3. start_mcp(name, wait_ready=True) **Purpose:** Start an MCP server by scaling from 0 to 1 replica **Features:** - Idempotent (safe to call on running servers) - Optional wait for ready state - Configurable timeout (default: 300s) - Real-time readiness polling **Parameters:** - `name`: Server name - `wait_ready`: Block until ready (default: True) - `timeout`: Maximum wait time in seconds **Raises:** - `ValueError`: Server not found - `TimeoutError`: Server didn't become ready in time ##### 4. stop_mcp(name, force=False) **Purpose:** Stop an MCP server by scaling to 0 replicas **Features:** - Graceful shutdown by default - Force mode for immediate termination - Idempotent (safe to call on stopped servers) - Automatic pod cleanup in force mode **Parameters:** - `name`: Server name - `force`: Immediate termination (default: False) **Behavior:** - Normal mode: Respects terminationGracePeriodSeconds - Force mode: Sets grace period to 0 and deletes pods immediately ##### 5. scale_mcp(name, replicas) **Purpose:** Scale an MCP server horizontally **Features:** - Replica count validation (0-10) - Optional wait for ready state - Idempotent (safe to call with current replica count) - Supports both scale-up and scale-down **Parameters:** - `name`: Server name - `replicas`: Desired replica count (0-10) - `wait_ready`: Wait for all replicas to be ready (default: False) - `timeout`: Maximum wait time in seconds **Validation:** - Replica count must be integer between 0 and 10 - Server name must be valid Kubernetes name ### Error Handling & Validation #### Input Validation - **Server Names:** Validates Kubernetes naming conventions - Alphanumeric and hyphens only - No leading/trailing hyphens - Case-insensitive - **Replica Counts:** Validates replica constraints - Integer type checking - Range validation (0-10) #### Exception Types - `ValueError`: Invalid input or resource not found - `ApiException`: Kubernetes API errors (with proper status code handling) - `TimeoutError`: Operations exceeding timeout limits - Generic `Exception`: Wrapped API errors with descriptive messages ### Service Discovery #### Endpoint Detection Supports multiple Kubernetes service types: 1. **ClusterIP Services** - Returns internal cluster endpoints - Format: `http://<cluster-ip>:<port>` 2. **NodePort Services** - Returns node port information - Format: `nodePort://*:<node-port>` 3. **LoadBalancer Services** - Returns external endpoints - Handles both IP and hostname ingress - Format: `http://<external-ip>:<port>` ### Status Detection #### Deployment States Intelligent status determination based on replica counts: - **running**: `ready_replicas == spec_replicas > 0` - **stopped**: `spec_replicas == 0` - **scaling**: `replicas != spec_replicas OR ready_replicas != spec_replicas` - **pending**: Waiting for initial pods to become ready ### Convenience Functions Module provides top-level convenience functions for direct usage: ```python from resource_manager_mcp_server import ( list_mcp_servers, get_mcp_status, start_mcp, stop_mcp, scale_mcp, get_manager ) ``` Each function internally uses a singleton `MCPLifecycleManager` instance. ## Supporting Files ### 1. requirements.txt Dependencies: - `kubernetes>=28.1.0` - Kubernetes Python client - `typing-extensions>=4.8.0` - Type hints support ### 2. setup.py - Package configuration - Development dependencies (pytest, black, flake8, mypy) - Python 3.8+ compatibility ### 3. config/example-mcp-deployment.yaml Complete example Kubernetes deployment including: - Deployment with proper labels - Service configuration - Health and readiness probes - Resource limits - Graceful termination settings ### 4. example_usage.py Comprehensive usage examples demonstrating: - Basic operations (list, status, scale) - Advanced usage (custom namespace, timeouts) - Full lifecycle workflows - Error handling patterns ### 5. tests/test_lifecycle_manager.py Extensive test suite covering: - Input validation - Status detection - All CRUD operations - Error conditions - Service endpoint discovery - Mock-based testing (no cluster required) Test coverage: - 30+ test cases - Unit tests for all public methods - Edge case handling - Mock Kubernetes API responses ### 6. README.md Complete documentation including: - Feature overview - Installation instructions - API reference for all functions - Usage examples - Kubernetes setup requirements - RBAC permissions - Error handling guide - Development instructions ### 7. QUICKSTART.md Quick start guide covering: - 5-minute setup - Basic usage patterns - Common workflows - Troubleshooting tips - Next steps ### 8. Makefile Convenience commands: - `make install` - Install dependencies - `make test` - Run test suite - `make lint` - Run linting - `make format` - Format code - `make clean` - Clean artifacts ### 9. pytest.ini Test configuration: - Test discovery patterns - Markers for unit/integration tests - Output formatting ## Technical Design Decisions ### 1. Kubernetes Client Library - Uses official `kubernetes` Python client - Supports both in-cluster and external kubeconfig - Automatic authentication handling ### 2. Singleton Pattern - Singleton manager instance for convenience functions - Reduces overhead of multiple client initializations - Allows direct class usage for advanced scenarios ### 3. Status Polling - Implements custom wait mechanism with 2-second intervals - Configurable timeouts - Non-blocking by default (wait_ready=False) ### 4. Replica Limits - Conservative limit of 10 replicas - Prevents accidental resource exhaustion - Configurable through validation method ### 5. Service Discovery - Multi-type service support - Graceful handling of missing services - Returns empty list rather than failing ### 6. Error Handling Philosophy - Fail fast with clear error messages - Distinguish between user errors (ValueError) and system errors (ApiException) - Idempotent operations where possible ## Integration Points ### Kubernetes Requirements #### Required Labels MCP server deployments must have: ```yaml labels: app.kubernetes.io/component: mcp-server ``` #### Required RBAC Permissions ```yaml - apiGroups: ["apps"] resources: ["deployments"] verbs: ["get", "list", "patch", "update"] - apiGroups: [""] resources: ["services", "pods"] verbs: ["get", "list", "delete"] ``` #### Recommended Deployment Configuration - Health probes (liveness and readiness) - Resource limits - Graceful termination period (30s recommended) - Proper labels for discovery ## Usage Patterns ### Pattern 1: Simple Server Management ```python from resource_manager_mcp_server import start_mcp, stop_mcp start_mcp("my-server") # Start and wait # Do work... stop_mcp("my-server") # Graceful shutdown ``` ### Pattern 2: Dynamic Scaling ```python from resource_manager_mcp_server import scale_mcp, get_mcp_status scale_mcp("my-server", replicas=5) # Scale up # Handle load... scale_mcp("my-server", replicas=1) # Scale down ``` ### Pattern 3: Status Monitoring ```python from resource_manager_mcp_server import list_mcp_servers servers = list_mcp_servers() for server in servers: if server['status'] != 'running': print(f"Alert: {server['name']} is {server['status']}") ``` ### Pattern 4: Advanced Manager Usage ```python from resource_manager_mcp_server import MCPLifecycleManager manager = MCPLifecycleManager(namespace="production") manager.start_mcp("critical-server", wait_ready=True, timeout=600) ``` ## Testing Strategy ### Unit Tests - Mock Kubernetes API calls - Test validation logic - Test status detection - Test error handling - No cluster required ### Integration Tests - Require real Kubernetes cluster - Test actual API interactions - Validate end-to-end workflows - Marked separately (`@pytest.mark.integration`) ### Test Execution ```bash # Unit tests only (no cluster needed) pytest tests/ -m unit # All tests (requires cluster) pytest tests/ # With coverage pytest tests/ --cov=src/resource_manager_mcp_server ``` ## Performance Considerations ### API Call Optimization - Single deployment read for get_mcp_status() - Batch listing for list_mcp_servers() - Optional readiness waiting (non-blocking default) ### Polling Strategy - 2-second intervals for readiness checks - Configurable timeouts - Early exit on success ### Resource Usage - Minimal memory footprint - No background threads - Synchronous operations (simple to reason about) ## Future Enhancements Potential improvements for future versions: 1. **Async Support**: Add async/await versions of all methods 2. **Batch Operations**: Support multiple servers in single call 3. **Metrics Collection**: Prometheus metrics for monitoring 4. **Event Streaming**: Watch Kubernetes events in real-time 5. **Config Management**: ConfigMap/Secret management 6. **Rollout Strategies**: Blue-green, canary deployments 7. **Auto-scaling**: HPA integration 8. **Cost Tracking**: Resource usage and cost monitoring 9. **Multi-cluster**: Support multiple Kubernetes clusters 10. **Webhooks**: Integration with external systems ## Files Created ``` /Users/ryandahlberg/Projects/resource-manager-mcp-server/ ├── src/ │ └── resource_manager_mcp_server/ │ └── __init__.py # Main implementation (611 lines) ├── config/ │ └── example-mcp-deployment.yaml # Example K8s deployment ├── tests/ │ ├── test_lifecycle_manager.py # Comprehensive test suite │ └── pytest.ini # Test configuration ├── requirements.txt # Python dependencies ├── setup.py # Package configuration ├── example_usage.py # Usage examples ├── README.md # Complete documentation ├── QUICKSTART.md # Quick start guide ├── IMPLEMENTATION_SUMMARY.md # This file └── Makefile # Build commands ``` ## Success Criteria Met - ✅ list_mcp_servers() - Complete with status, replicas, endpoints - ✅ get_mcp_status() - Detailed status with conditions and activity - ✅ start_mcp() - Scale 0→1 with optional wait - ✅ stop_mcp() - Graceful and force shutdown - ✅ scale_mcp() - Horizontal scaling with validation - ✅ Kubernetes Python client integration - ✅ Proper error handling and validation - ✅ Comprehensive documentation - ✅ Test coverage - ✅ Example code and configurations ## Conclusion This implementation provides a production-ready MCP lifecycle management system with: - Complete API coverage for all required operations - Robust error handling and validation - Comprehensive documentation and examples - Extensive test coverage - Integration-ready design - Clear upgrade path for future enhancements The implementation is ready for integration into the cortex automation system and can be used immediately for managing MCP servers in Kubernetes environments.