Katamari MCP Server

# Phase 3: Advanced Agency Guide ## Overview Phase 3 transforms Katamari MCP from an adaptive learning system into a sophisticated agency with advanced workflow optimization, predictive capabilities, cross-component learning, and self-healing mechanisms. ## Architecture ### Core Components #### 1. Workflow Optimizer (`katamari_mcp/acp/workflow_optimizer.py`) **Purpose**: Optimize capability execution through parallel processing and pattern recognition. **Key Features**: - **Parallel Execution**: Identify and execute independent steps concurrently - **Pattern Recognition**: Learn optimal execution patterns from historical data - **Auto-Optimization**: Continuously improve workflow performance - **Dependency Management**: Handle complex workflow dependencies - **Resource Optimization**: Balance resource usage across workflow steps **Usage**: ```python from katamari_mcp.acp.workflow_optimizer import WorkflowOptimizer, WorkflowDefinition, WorkflowStep # Create optimizer optimizer = WorkflowOptimizer() # Define workflow steps steps = [ WorkflowStep( step_id="data_validation", capability_id="validator", step_name="Validate Data", depends_on=[], timeout=30.0, can_parallelize=True ), WorkflowStep( step_id="data_processing", capability_id="processor", step_name="Process Data", depends_on=["data_validation"], timeout=60.0, can_parallelize=False ) ] # Create workflow workflow = WorkflowDefinition( workflow_id="data_pipeline", workflow_name="Data Processing Pipeline", steps=steps, pattern=WorkflowPattern.SEQUENTIAL ) # Register and execute workflow_id = await optimizer.register_workflow(workflow) execution = await optimizer.execute_workflow(workflow_id) ``` #### 2. Predictive Analytics Engine (`katamari_mcp/acp/predictive_engine.py`) **Purpose**: Predict performance issues and resource needs before they impact operations. **Key Features**: - **Performance Prediction**: Forecast execution time degradation - **Error Spike Detection**: Predict increased error rates - **Resource Forecasting**: Anticipate memory/CPU needs - **Confidence Scoring**: Provide confidence levels for predictions - **Multi-Horizon Analysis**: Short, medium, and long-term predictions **Usage**: ```python from katamari_mcp.acp.predictive_engine import PredictiveEngine # Create engine engine = PredictiveEngine() # Add performance data await engine.add_performance_data( capability_id="web_search", value=1.5, # execution time in seconds timestamp=datetime.now() ) # Add error data await engine.add_error_data( capability_id="web_search", error_type="TimeoutError", timestamp=datetime.now() ) # Add resource data await engine.add_resource_data( capability_id="web_search", value=85.5, # CPU usage percentage timestamp=datetime.now() ) # Get predictions predictions = engine.predictions.get("web_search", []) for prediction in predictions: print(f"Prediction: {prediction.prediction_type}") print(f"Confidence: {prediction.confidence}") print(f"Signal: {prediction.signal_value}") ``` #### 3. Knowledge Transfer Engine (`katamari_mcp/acp/knowledge_transfer.py`) **Purpose**: Enable learning and knowledge sharing between different capabilities. **Key Features**: - **Artifact Extraction**: Extract reusable knowledge from execution data - **Similarity Analysis**: Find similar capabilities for knowledge transfer - **Transfer Proposals**: Suggest knowledge transfers between components - **Learning Pathways**: Create structured learning paths for capabilities - **Cross-Component Insights**: Generate insights from cross-capability analysis **Usage**: ```python from katamari_mcp.acp.knowledge_transfer import KnowledgeTransferEngine # Create engine engine = KnowledgeTransferEngine() # Register capability profile await engine.register_capability_profile( component_id="data_processor", profile={ "capability_id": "data_processor", "description": "Processes JSON data efficiently", "tags": ["json", "processing", "optimization"], "performance_metrics": {"avg_execution_time": 0.5}, "learning_patterns": ["batch_processing", "caching"] } ) # Extract knowledge from learning records learning_record = { "capability_id": "data_processor", "learning_signal": "optimization_success", "context": {"input_size": "large", "batch_enabled": True}, "outcome": "success", "improvement": 0.3 } artifacts = await engine.extract_knowledge("data_processor", learning_record) # Analyze similarity between capabilities similarity = await engine._calculate_capability_similarity("data_processor", "json_validator") print(f"Similarity score: {similarity}") ``` #### 4. Self-Healing Engine (`katamari_mcp/acp/self_healing.py`) **Purpose**: Automatically detect, analyze, and recover from system errors. **Key Features**: - **Error Pattern Recognition**: Identify recurring error patterns - **Multiple Healing Strategies**: Retry, restart, failover, degradation - **Health Monitoring**: Continuous capability health assessment - **Resilience Policies**: Configurable healing strategies per capability - **Proactive Recovery**: Address issues before they cause failures **Usage**: ```python from katamari_mcp.acp.self_healing import SelfHealingEngine # Create engine engine = SelfHealingEngine() # Report error await engine.report_error( capability_id="web_search", error_type="NetworkError", error_message="Connection timeout", context={"timeout": 30, "retry_count": 0} ) # Get health status health = engine.get_health_status("web_search") print(f"Health status: {health.status}") print(f"Error count: {health.error_count}") print(f"Last error: {health.last_error_time}") # Configure resilience policy policy = ResiliencePolicy( capability_id="web_search", max_retries=3, retry_delay=1.0, circuit_breaker_threshold=5, healing_strategies=[HealingMethod.RETRY, HealingMethod.FAILOVER] ) ``` ## Integration ### Server Integration Phase 3 components are automatically integrated into the main KatamariServer: ```python from katamari_mcp.server import KatamariServer # Create server with Phase 3 enabled server = KatamariServer({ "phase3_enabled": True }) # Components are available as: server.workflow_optimizer server.predictive_engine server.knowledge_transfer server.self_healing ``` ### Configuration Enable/disable Phase 3 components in configuration: ```python # config.py or environment PHASE3_ENABLED = True WORKFLOW_OPTIMIZER_ENABLED = True PREDICTIVE_ENGINE_ENABLED = True KNOWLEDGE_TRANSFER_ENABLED = True SELF_HEALING_ENABLED = True ``` ## Testing ### Running Tests ```bash # Basic Phase 3 functionality tests pytest tests/test_phase3_simple.py -v # Integration tests pytest tests/test_phase3_integration.py -v # All Phase 3 tests pytest tests/test_phase3_simple.py tests/test_phase3_integration.py -v ``` ### Test Coverage - **Import Tests**: Verify all components can be imported - **Initialization Tests**: Verify components initialize correctly - **Basic Functionality Tests**: Test core methods and features - **Integration Tests**: Test component interaction and data flow - **System Resilience Tests**: Test system behavior under stress ## Performance Considerations ### Resource Usage - **Workflow Optimizer**: Low to moderate memory usage, CPU intensive during optimization - **Predictive Engine**: Moderate memory for historical data, CPU intensive during analysis - **Knowledge Transfer**: Low to moderate memory, CPU intensive during similarity analysis - **Self-Healing**: Low memory usage, minimal CPU overhead ### Scaling - **Horizontal Scaling**: Components can be distributed across multiple instances - **Data Retention**: Configurable data retention policies for performance - **Caching**: Built-in caching for frequently accessed data - **Batch Processing**: Batch operations for improved performance ## Monitoring ### Health Endpoints Phase 3 components expose health information: ```python # Server status includes Phase 3 information status = await server._handle_server_status({}) print(status["phase3_enabled"]) print(status["phase3_components"]) ``` ### Metrics Each component tracks its own metrics: - **Workflow Optimizer**: Execution time, optimization success rate, parallelization efficiency - **Predictive Engine**: Prediction accuracy, confidence scores, false positive rates - **Knowledge Transfer**: Artifact count, transfer success rate, similarity scores - **Self-Healing**: Error detection rate, healing success rate, recovery time ## Best Practices ### Workflow Design 1. **Identify Independence**: Mark steps that can run in parallel 2. **Set Realistic Timeouts**: Avoid overly aggressive timeouts 3. **Handle Dependencies**: Clearly define step dependencies 4. **Monitor Performance**: Track workflow execution metrics ### Predictive Analytics 1. **Provide Quality Data**: Ensure accurate performance data 2. **Regular Updates**: Feed data consistently for better predictions 3. **Monitor Confidence**: Track prediction confidence levels 4. **Act on Alerts**: Respond to high-confidence predictions ### Knowledge Transfer 1. **Document Capabilities**: Provide detailed capability profiles 2. **Enable Learning**: Allow capabilities to share knowledge 3. **Review Transfers**: Validate knowledge transfer proposals 4. **Track Impact**: Monitor the impact of transferred knowledge ### Self-Healing 1. **Configure Policies**: Set appropriate healing strategies 2. **Monitor Health**: Track capability health metrics 3. **Test Recovery**: Verify healing mechanisms work 4. **Adjust Strategies**: Refine healing approaches based on results ## Troubleshooting ### Common Issues 1. **Import Errors**: Ensure all dependencies are installed 2. **Performance Issues**: Check resource usage and data retention policies 3. **Integration Problems**: Verify component configuration and connectivity 4. **Test Failures**: Check test environment and data setup ### Debug Mode Enable debug logging for detailed information: ```python import logging logging.getLogger("katamari_mcp.acp.workflow_optimizer").setLevel(logging.DEBUG) logging.getLogger("katamari_mcp.acp.predictive_engine").setLevel(logging.DEBUG) logging.getLogger("katamari_mcp.acp.knowledge_transfer").setLevel(logging.DEBUG) logging.getLogger("katamari_mcp.acp.self_healing").setLevel(logging.DEBUG) ``` ## Future Enhancements ### Planned Features - **Advanced ML Models**: Integration with more sophisticated ML models - **Distributed Execution**: Support for distributed workflow execution - **Real-time Collaboration**: Multi-agent collaboration capabilities - **Advanced Analytics**: More sophisticated analytics and reporting - **Custom Healing Strategies**: User-defined healing strategies ### Research Areas - **Reinforcement Learning**: For workflow optimization - **Anomaly Detection**: Advanced anomaly detection for predictive analytics - **Graph Neural Networks**: For knowledge similarity analysis - **Causal Inference**: For root cause analysis in self-healing ## Conclusion Phase 3 represents a significant evolution of the Katamari MCP system, transforming it from an adaptive learning platform into a sophisticated agency capable of self-optimization, prediction, learning, and healing. The modular design ensures that each component can be used independently or together to create powerful, intelligent systems. The comprehensive test suite and integration with the main server ensure that Phase 3 features are production-ready and can be immediately leveraged to create more capable and resilient MCP applications.