Claude MCP Server Ecosystem

# Week 12: Advanced AI Integration Server ## 🎯 Objective Build comprehensive AI integration capabilities with model orchestration, ensemble methods, AutoML, neural architecture search, and AI operations. ## 📋 Components (5 Required) ### 1. Model Orchestration Engine **File**: `servers/ai-integration/src/model-orchestration.ts` **Purpose**: Coordinate multiple AI models and services **Key Features**: - Multi-model pipeline management - Model composition and chaining - Resource allocation and scheduling - Cross-model communication protocols - Performance optimization **MCP Tools**: - `create_model_pipeline` - `execute_orchestration` - `get_orchestration_status` - `optimize_pipeline` ### 2. Ensemble Methods Service **File**: `servers/ai-integration/src/ensemble-methods.ts` **Purpose**: Combine multiple models for improved predictions **Key Features**: - Voting classifiers and regressors - Stacking and blending algorithms - Bagging and boosting methods - Dynamic ensemble selection - Performance benchmarking **MCP Tools**: - `create_ensemble` - `train_ensemble` - `predict_ensemble` - `evaluate_ensemble` ### 3. AutoML Service **File**: `servers/ai-integration/src/automl-service.ts` **Purpose**: Automated machine learning and hyperparameter optimization **Key Features**: - Automated feature engineering - Model selection and hyperparameter tuning - Neural architecture search - Automated pipeline generation - Performance tracking **MCP Tools**: - `start_automl_experiment` - `get_automl_results` - `deploy_best_model` - `optimize_hyperparameters` ### 4. Neural Architecture Search **File**: `servers/ai-integration/src/neural-architecture-search.ts` **Purpose**: Automated discovery of optimal neural network architectures **Key Features**: - Architecture search algorithms - Performance estimation strategies - Progressive search methods - Multi-objective optimization - Resource-aware architecture design **MCP Tools**: - `start_nas_experiment` - `get_architecture_candidates` - `evaluate_architecture` - `deploy_discovered_architecture` ### 5. AI Operations (AIOps) **File**: `servers/ai-integration/src/ai-operations.ts` **Purpose**: Operational management of AI systems and workflows **Key Features**: - Model lifecycle management - Performance monitoring and alerting - Automated model retraining - Drift detection and remediation - Resource optimization **MCP Tools**: - `monitor_model_performance` - `detect_model_drift` - `trigger_retraining` - `manage_model_lifecycle` ## 🏗️ Implementation Structure ``` servers/ai-integration/ ├── src/ │ ├── model-orchestration.ts # Component 1 │ ├── ensemble-methods.ts # Component 2 │ ├── automl-service.ts # Component 3 │ ├── neural-architecture-search.ts # Component 4 │ ├── ai-operations.ts # Component 5 │ └── index.ts # Server entry point ├── test/ │ ├── model-orchestration.test.ts │ ├── ensemble-methods.test.ts │ ├── automl-service.test.ts │ ├── neural-architecture-search.test.ts │ └── ai-operations.test.ts ├── examples/ │ ├── orchestration-demo.js │ ├── ensemble-example.js │ ├── automl-workflow.js │ ├── nas-experiment.js │ └── aiops-monitoring.js ├── package.json └── README.md ``` ## 🔧 Technical Requirements ### Dependencies ```json { "dependencies": { "@tensorflow/tfjs": "^4.0.0", "optuna": "^0.1.0", "ray": "^2.0.0", "mlflow": "^2.0.0", "kubeflow-pipelines": "^1.0.0", "hyperopt": "^0.1.0", "auto-sklearn": "^0.15.0" } } ``` ### Integration Points - Extends `BaseServer` from shared utilities - Uses `withPerformanceMonitoring` decorator - Implements `HealthChecker` interface - Integrates with existing model deployment infrastructure - Connects to data pipeline and warehouse systems ## ✅ Success Criteria ### Implementation Complete When: - [ ] All 5 components implemented with full TypeScript interfaces - [ ] MCP server classes extend BaseServer correctly - [ ] All MCP tools properly defined and functional - [ ] Comprehensive error handling with MCPError types - [ ] Performance monitoring decorators applied - [ ] Health checking endpoints operational ### Testing Complete When: - [ ] Unit tests for all core functions (>90% coverage) - [ ] Integration tests between components - [ ] End-to-end workflow tests - [ ] Performance benchmarking tests - [ ] Error scenario testing ### Documentation Complete When: - [ ] Component README files with usage examples - [ ] MCP tool documentation with schemas - [ ] API endpoint documentation - [ ] Integration examples with existing systems - [ ] Troubleshooting guide ## 🚀 Getting Started ### Setup Week 12 Environment: ```bash # Create directory structure mkdir -p servers/ai-integration/{src,test,examples} # Initialize package.json npm init -y --prefix servers/ai-integration # Install dependencies cd servers/ai-integration && npm install [dependencies] # Copy base server template cp shared/templates/base-server-template.ts src/index.ts ``` ### Development Order: 1. **Model Orchestration** - Foundation for other components 2. **Ensemble Methods** - Build on orchestration framework 3. **AutoML Service** - Leverage ensemble capabilities 4. **Neural Architecture Search** - Extend AutoML functionality 5. **AI Operations** - Monitor and manage all components ### Testing Strategy: ```bash # Run component tests npm run test:week-12 # Run integration tests npm run test:integration:week-12 # Run performance tests npm run test:performance:week-12 # Run end-to-end tests npm run test:e2e:week-12 ``` ## 📊 Expected Outcomes By the end of Week 12, the system will support: - Orchestrated execution of complex AI workflows - Automated ensemble learning with multiple algorithms - Complete AutoML pipelines from data to deployment - Automated neural architecture discovery and optimization - Comprehensive AI operations monitoring and management This completes the Advanced AI Integration Server and sets the foundation for Week 13's implementation. --- **Week 12 Status**: 🚧 READY TO START **Prerequisites**: Week 11 Data Analytics Server ✅ COMPLETE **Next Week**: Week 13 - Specialized Analysis Server