# TDD Mode MCP Tools Documentation

## Overview

This document provides comprehensive guidance for using SAFLA's MCP tools within the Test-Driven Development (TDD) mode. The TDD mode leverages SAFLA's 40+ MCP tools to create, execute, and optimize comprehensive test suites through systematic MCP interactions and agent coordination.

## Core Testing MCP Tools

### 1. Agent-Based Test Development

#### Test Agent Creation and Management

**Primary Tool: [`create_agent_session`](safla/mcp_stdio_server.py:1)**
```bash
# Create specialized TDD agent
use_mcp_tool safla create_agent_session '{
  "agent_type": "cognitive",
  "session_config": {
    "focus": "test_driven_development",
    "testing_framework": "jest",
    "coverage_target": 95,
    "test_types": ["unit", "integration", "performance"],
    "quality_focus": "comprehensive"
  },
  "timeout_seconds": 7200
}'

# Expected Response:
{
  "session_id": "tdd_agent_001",
  "status": "active",
  "capabilities": [
    "test_generation",
    "coverage_analysis", 
    "assertion_optimization",
    "mock_creation",
    "performance_testing"
  ],
  "configuration": {
    "framework": "jest",
    "target_coverage": 95,
    "test_strategy": "comprehensive"
  }
}
```

**Agent Interaction for Test Generation: [`interact_with_agent`](safla/mcp_stdio_server.py:1)**
```bash
# Generate comprehensive test suite
use_mcp_tool safla interact_with_agent '{
  "session_id": "tdd_agent_001",
  "command": "generate_test_suite",
  "parameters": {
    "module_name": "data_processor",
    "requirements": [
      "validate_input_data",
      "transform_data_format", 
      "handle_edge_cases",
      "performance_requirements"
    ],
    "test_types": ["unit", "integration", "performance"],
    "coverage_target": 95
  }
}'

# Expected Response:
{
  "test_suite": {
    "unit_tests": 24,
    "integration_tests": 8,
    "performance_tests": 4,
    "total_assertions": 156
  },
  "coverage_estimate": 96.2,
  "test_files": [
    "data_processor.unit.test.ts",
    "data_processor.integration.test.ts", 
    "data_processor.performance.test.ts"
  ]
}
```

**Agent Session Management: [`list_agent_sessions`](safla/mcp_stdio_server.py:1)**
```bash
# Monitor active testing agents
use_mcp_tool safla list_agent_sessions '{
  "filter_by_type": "cognitive",
  "include_inactive": false
}'

# Expected Response:
{
  "active_sessions": [
    {
      "session_id": "tdd_agent_001",
      "agent_type": "cognitive",
      "focus": "test_driven_development",
      "status": "active",
      "uptime": "2h 15m",
      "tasks_completed": 12
    },
    {
      "session_id": "perf_test_agent_002", 
      "agent_type": "cognitive",
      "focus": "performance_testing",
      "status": "active",
      "uptime": "45m",
      "tasks_completed": 3
    }
  ]
}
```

### 2. Test Execution and Validation

#### Comprehensive Test Execution: [`run_integration_tests`](safla/mcp_stdio_server.py:1)
```bash
# Execute comprehensive test suite
use_mcp_tool safla run_integration_tests '{
  "test_suite": "comprehensive_validation",
  "parallel": true,
  "verbose": true
}'

# Expected Response:
{
  "execution_summary": {
    "total_tests": 156,
    "passed": 152,
    "failed": 4,
    "skipped": 0,
    "execution_time": "2m 34s"
  },
  "coverage": {
    "line_coverage": 94.7,
    "branch_coverage": 92.1,
    "function_coverage": 98.3
  },
  "failed_tests": [
    "data_processor.unit.test.ts:45 - async timeout",
    "data_processor.integration.test.ts:23 - mock assertion failed"
  ]
}

# Execute specific test categories
use_mcp_tool safla run_integration_tests '{
  "test_suite": "unit_tests_only",
  "parallel": true,
  "verbose": false
}'

# Performance-focused test execution
use_mcp_tool safla run_integration_tests '{
  "test_suite": "performance_validation",
  "parallel": false,
  "verbose": true
}'
```

#### Memory Operations Testing: [`validate_memory_operations`](safla/mcp_stdio_server.py:1)
```bash
# Validate memory operations in tests
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 200,
  "include_stress_test": true
}'

# Expected Response:
{
  "validation_results": {
    "memory_leaks": "none_detected",
    "allocation_efficiency": 94.2,
    "deallocation_success": 100.0,
    "stress_test_passed": true
  },
  "performance_metrics": {
    "avg_allocation_time": "0.23ms",
    "avg_deallocation_time": "0.18ms",
    "peak_memory_usage": "1.2GB"
  },
  "recommendations": [
    "consider_object_pooling_for_frequent_allocations",
    "optimize_large_object_handling"
  ]
}

# Memory validation with custom parameters
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 500,
  "include_stress_test": true
}'
```

#### MCP Connectivity Testing: [`test_mcp_connectivity`](safla/mcp_stdio_server.py:1)
```bash
# Test MCP server connectivity for testing infrastructure
use_mcp_tool safla test_mcp_connectivity '{
  "target_server": "safla",
  "test_depth": "comprehensive"
}'

# Expected Response:
{
  "connectivity_status": "healthy",
  "response_time": "12ms",
  "protocol_compliance": "full",
  "tool_availability": {
    "total_tools": 40,
    "available_tools": 40,
    "unavailable_tools": 0
  },
  "resource_access": {
    "total_resources": 15,
    "accessible_resources": 15,
    "inaccessible_resources": 0
  }
}

# Basic connectivity test
use_mcp_tool safla test_mcp_connectivity '{
  "target_server": "safla",
  "test_depth": "basic"
}'
```

### 3. Performance Testing and Benchmarking

#### Memory Performance Testing: [`benchmark_memory_performance`](safla/mcp_stdio_server.py:1)
```bash
# Comprehensive memory performance testing
use_mcp_tool safla benchmark_memory_performance '{
  "test_duration": 300,
  "memory_patterns": ["sequential", "random", "mixed", "stress_test"]
}'

# Expected Response:
{
  "benchmark_results": {
    "sequential_access": {
      "throughput": "2.1GB/s",
      "latency": "0.45ms avg",
      "efficiency": 94.2
    },
    "random_access": {
      "throughput": "1.3GB/s", 
      "latency": "0.78ms avg",
      "efficiency": 87.6
    },
    "mixed_patterns": {
      "throughput": "1.8GB/s",
      "latency": "0.56ms avg", 
      "efficiency": 91.3
    }
  },
  "stress_test_results": {
    "max_sustained_load": "85% system memory",
    "degradation_point": "90% system memory",
    "recovery_time": "1.2s"
  }
}

# Focused performance testing for specific patterns
use_mcp_tool safla benchmark_memory_performance '{
  "test_duration": 180,
  "memory_patterns": ["production_simulation"]
}'
```

#### Vector Operations Benchmarking: [`benchmark_vector_operations`](safla/mcp_stdio_server.py:1)
```bash
# Benchmark vector operations for performance testing
use_mcp_tool safla benchmark_vector_operations '{
  "vector_count": 10000,
  "vector_dimensions": 512,
  "operations": ["similarity_search", "nearest_neighbor", "clustering", "classification"]
}'

# Expected Response:
{
  "benchmark_results": {
    "similarity_search": {
      "avg_time": "23ms",
      "throughput": "434 ops/s",
      "accuracy": 98.7
    },
    "nearest_neighbor": {
      "avg_time": "18ms",
      "throughput": "555 ops/s", 
      "accuracy": 99.2
    },
    "clustering": {
      "avg_time": "156ms",
      "throughput": "64 ops/s",
      "quality_score": 94.1
    }
  },
  "optimization_recommendations": [
    "enable_gpu_acceleration",
    "increase_batch_size_to_750",
    "implement_parallel_processing"
  ]
}

# Performance testing for specific vector operations
use_mcp_tool safla benchmark_vector_operations '{
  "vector_count": 5000,
  "vector_dimensions": 256,
  "operations": ["similarity_search"]
}'
```

#### MCP Throughput Testing: [`benchmark_mcp_throughput`](safla/mcp_stdio_server.py:1)
```bash
# Test MCP protocol performance for testing infrastructure
use_mcp_tool safla benchmark_mcp_throughput '{
  "request_count": 1000,
  "concurrent_connections": 10,
  "payload_size": "large"
}'

# Expected Response:
{
  "throughput_metrics": {
    "requests_per_second": 847,
    "avg_response_time": "11.8ms",
    "95th_percentile": "23.4ms",
    "99th_percentile": "45.2ms"
  },
  "concurrency_performance": {
    "max_concurrent": 10,
    "connection_overhead": "2.1ms",
    "resource_utilization": "moderate"
  },
  "payload_analysis": {
    "large_payload_impact": "15% latency increase",
    "compression_benefit": "23% size reduction",
    "optimal_payload_size": "medium"
  }
}
```

### 4. Learning and Adaptation for Testing

#### Learning Cycle Integration: [`trigger_learning_cycle`](safla/mcp_stdio_server.py:1)
```bash
# Trigger learning cycle for test optimization
use_mcp_tool safla trigger_learning_cycle '{
  "learning_type": "meta",
  "data_sources": ["test_results", "coverage_data", "performance_metrics", "failure_patterns"],
  "focus_areas": ["test_effectiveness", "coverage_optimization", "performance_testing"]
}'

# Expected Response:
{
  "learning_session_id": "test_learn_001",
  "status": "active",
  "learning_targets": [
    "test_pattern_optimization",
    "coverage_gap_identification", 
    "performance_test_enhancement",
    "failure_prediction"
  ],
  "data_processing": {
    "test_results_analyzed": 1247,
    "patterns_identified": 23,
    "optimization_opportunities": 8
  }
}

# Reinforcement learning for test improvement
use_mcp_tool safla trigger_learning_cycle '{
  "learning_type": "reinforcement",
  "data_sources": ["test_failures", "fix_outcomes"],
  "focus_areas": ["failure_prevention", "test_reliability"]
}'
```

#### Learning Metrics Analysis: [`get_learning_metrics`](safla/mcp_stdio_server.py:1)
```bash
# Analyze learning metrics for testing improvements
use_mcp_tool safla get_learning_metrics '{
  "metric_type": "all",
  "time_range_hours": 168
}'

# Expected Response:
{
  "learning_metrics": {
    "test_accuracy_improvement": 12.3,
    "coverage_optimization": 8.7,
    "performance_prediction": 89.4,
    "failure_prevention": 76.2
  },
  "adaptation_success": {
    "successful_adaptations": 34,
    "failed_adaptations": 3,
    "success_rate": 91.9
  },
  "knowledge_retention": {
    "pattern_retention": 94.1,
    "strategy_retention": 87.6,
    "optimization_retention": 92.3
  }
}

# Focus on specific learning metrics
use_mcp_tool safla get_learning_metrics '{
  "metric_type": "accuracy",
  "time_range_hours": 72
}'
```

#### Learning Parameter Optimization: [`update_learning_parameters`](safla/mcp_stdio_server.py:1)
```bash
# Optimize learning parameters for testing
use_mcp_tool safla update_learning_parameters '{
  "learning_rate": 0.15,
  "adaptation_threshold": 0.1,
  "memory_retention": 0.92,
  "exploration_factor": 0.18
}'

# Expected Response:
{
  "parameter_update": "successful",
  "previous_parameters": {
    "learning_rate": 0.12,
    "adaptation_threshold": 0.08,
    "memory_retention": 0.88,
    "exploration_factor": 0.15
  },
  "expected_impact": {
    "learning_speed": "15% increase",
    "adaptation_sensitivity": "25% increase",
    "knowledge_retention": "4.5% increase"
  }
}
```

### 5. Strategy and Goal Management for Testing

#### Testing Strategy Selection: [`select_optimal_strategy`](safla/mcp_stdio_server.py:1)
```bash
# Select optimal testing strategy
use_mcp_tool safla select_optimal_strategy '{
  "context": "comprehensive_test_suite_development",
  "constraints": {
    "time_budget": "4_hours",
    "coverage_target": "95%",
    "performance_requirements": "high"
  },
  "objectives": ["coverage", "reliability", "maintainability", "performance"]
}'

# Expected Response:
{
  "selected_strategy": {
    "strategy_id": "comprehensive_tdd_001",
    "strategy_name": "performance_focused_comprehensive_testing",
    "effectiveness_score": 94.2,
    "estimated_duration": "3.5_hours"
  },
  "strategy_components": [
    "unit_test_generation",
    "integration_test_development",
    "performance_benchmark_creation",
    "edge_case_identification"
  ],
  "expected_outcomes": {
    "coverage": "96.3%",
    "reliability_score": 92.1,
    "maintainability_index": 87.4
  }
}
```

#### Custom Testing Strategy Creation: [`create_custom_strategy`](safla/mcp_stdio_server.py:1)
```bash
# Create custom testing strategy
use_mcp_tool safla create_custom_strategy '{
  "strategy_name": "adaptive_performance_testing",
  "description": "Adaptive testing strategy with performance focus and learning integration",
  "context": "performance_critical_testing",
  "steps": [
    "establish_performance_baselines",
    "generate_adaptive_test_cases",
    "execute_performance_validation",
    "analyze_and_optimize_results",
    "update_testing_patterns"
  ],
  "expected_outcomes": [
    "comprehensive_performance_coverage",
    "adaptive_test_optimization",
    "continuous_improvement"
  ]
}'

# Expected Response:
{
  "strategy_creation": "successful",
  "strategy_id": "adaptive_perf_test_001",
  "validation_status": "approved",
  "integration_points": [
    "performance_testing_framework",
    "learning_system",
    "optimization_engine"
  ]
}
```

#### Testing Goal Management: [`create_goal`](safla/mcp_stdio_server.py:1)
```bash
# Create comprehensive testing goal
use_mcp_tool safla create_goal '{
  "goal_name": "comprehensive_test_coverage",
  "description": "Achieve comprehensive test coverage with high quality and performance validation",
  "priority": "high",
  "target_metrics": {
    "line_coverage": 0.95,
    "branch_coverage": 0.92,
    "performance_test_coverage": 0.85,
    "quality_score": 0.90
  },
  "deadline": 1735689600,
  "dependencies": ["test_framework_setup", "performance_baseline_establishment"]
}'

# Expected Response:
{
  "goal_creation": "successful",
  "goal_id": "test_coverage_001",
  "status": "active",
  "tracking_enabled": true,
  "milestone_schedule": [
    "unit_test_completion",
    "integration_test_completion", 
    "performance_test_completion",
    "quality_validation"
  ]
}
```

#### Goal Progress Evaluation: [`evaluate_goal_progress`](safla/mcp_stdio_server.py:1)
```bash
# Evaluate testing goal progress
use_mcp_tool safla evaluate_goal_progress '{
  "goal_id": "test_coverage_001",
  "include_recommendations": true
}'

# Expected Response:
{
  "goal_progress": {
    "overall_progress": 0.78,
    "metric_progress": {
      "line_coverage": 0.92,
      "branch_coverage": 0.87,
      "performance_test_coverage": 0.71,
      "quality_score": 0.85
    }
  },
  "recommendations": [
    "focus_on_branch_coverage_improvement",
    "expand_performance_test_scenarios",
    "enhance_edge_case_testing"
  ],
  "estimated_completion": "2.5_days"
}
```

### 6. System Monitoring and Health for Testing

#### Performance Bottleneck Analysis: [`analyze_performance_bottlenecks`](safla/mcp_stdio_server.py:1)
```bash
# Analyze performance bottlenecks in testing infrastructure
use_mcp_tool safla analyze_performance_bottlenecks '{
  "duration_seconds": 600,
  "include_memory_profile": true
}'

# Expected Response:
{
  "bottlenecks_identified": [
    {
      "component": "test_execution_engine",
      "issue": "sequential_test_execution",
      "impact": "high",
      "recommendation": "implement_parallel_execution"
    },
    {
      "component": "mock_data_generation",
      "issue": "inefficient_data_creation",
      "impact": "medium", 
      "recommendation": "cache_mock_data_templates"
    }
  ],
  "memory_profile": {
    "peak_usage": "3.2GB",
    "average_usage": "1.8GB",
    "memory_leaks": "none_detected"
  }
}
```

#### System Health Monitoring: [`monitor_system_health`](safla/mcp_stdio_server.py:1)
```bash
# Monitor system health during testing
use_mcp_tool safla monitor_system_health '{
  "check_interval": 30,
  "alert_thresholds": {
    "test_failure_rate": 10,
    "performance_degradation": 15,
    "memory_usage": 80,
    "cpu_usage": 85
  }
}'

# Expected Response:
{
  "monitoring_session": "health_monitor_001",
  "status": "active",
  "current_health": {
    "overall_status": "healthy",
    "test_failure_rate": 3.2,
    "performance_score": 92.1,
    "resource_utilization": "optimal"
  },
  "alert_configuration": "active",
  "monitoring_duration": "continuous"
}
```

### 7. Resource Access for Testing

#### Test Results Resource: [`safla://test-results`](safla/mcp_stdio_server.py:1)
```bash
# Access real-time test results
access_mcp_resource safla "safla://test-results"

# Expected Response:
{
  "latest_test_run": {
    "timestamp": "2024-01-15T14:30:00Z",
    "total_tests": 234,
    "passed": 228,
    "failed": 6,
    "execution_time": "3m 45s"
  },
  "coverage_summary": {
    "line_coverage": 94.7,
    "branch_coverage": 91.2,
    "function_coverage": 97.8
  },
  "trending_data": {
    "pass_rate_trend": "improving",
    "coverage_trend": "stable",
    "performance_trend": "improving"
  }
}
```

#### Test Coverage Resource: [`safla://test-coverage`](safla/mcp_stdio_server.py:1)
```bash
# Access detailed coverage metrics
access_mcp_resource safla "safla://test-coverage"

# Expected Response:
{
  "coverage_details": {
    "overall_coverage": 94.7,
    "file_coverage": [
      {"file": "data_processor.ts", "coverage": 96.2},
      {"file": "validator.ts", "coverage": 98.1},
      {"file": "transformer.ts", "coverage": 91.3}
    ],
    "uncovered_lines": [
      {"file": "data_processor.ts", "lines": [45, 67, 89]},
      {"file": "transformer.ts", "lines": [23, 156, 178, 201]}
    ]
  },
  "quality_metrics": {
    "test_quality_score": 87.4,
    "maintainability_index": 82.1,
    "complexity_coverage": 89.7
  }
}
```

This comprehensive MCP tools documentation provides detailed guidance for leveraging SAFLA's capabilities within the TDD mode, ensuring effective test-driven development through systematic MCP tool usage and agent coordination.