# SAFLA MCP Tools for Orchestrator Mode

## Meta-Cognitive Engine Tools

### get_system_awareness()
**Purpose**: Retrieve current system self-awareness state and capabilities
**Usage**: Initialize workflow with current system understanding
```python
awareness = get_system_awareness()
# Returns: awareness_level, focus_areas, introspection_depth, self_assessment
```

### update_awareness_state(awareness_level, focus_areas, introspection_depth)
**Purpose**: Update system awareness parameters for adaptive behavior
**Usage**: Adjust system focus and awareness based on workflow needs
```python
update_awareness_state(
    awareness_level=0.8,
    focus_areas=["performance", "quality", "learning"],
    introspection_depth="deep"
)
```

### analyze_system_introspection(analysis_type, time_window_hours)
**Purpose**: Perform deep introspective analysis of system behavior
**Usage**: Analyze workflow patterns and performance for optimization
```python
introspection = analyze_system_introspection(
    analysis_type="comprehensive",
    time_window_hours=24
)
```

## Goal Management Tools

### create_goal(goal_name, description, priority, target_metrics, deadline, dependencies)
**Purpose**: Create new system goals with priorities and success metrics
**Usage**: Establish project objectives and tracking mechanisms
```python
create_goal(
    goal_name="aiGI_workflow_optimization",
    description="Optimize aiGI workflow for performance and quality",
    priority="high",
    target_metrics={
        "completion_time": 3600,
        "quality_score": 0.9,
        "error_rate": 0.05
    },
    deadline=time.time() + 86400,
    dependencies=["system_initialization", "resource_allocation"]
)
```

### list_goals(status_filter, priority_filter)
**Purpose**: List all active and completed goals with filtering
**Usage**: Monitor goal progress and prioritize workflow activities
```python
goals = list_goals(
    status_filter="active",
    priority_filter="high"
)
```

### evaluate_goal_progress(goal_id, include_recommendations)
**Purpose**: Assess progress toward specific goals with recommendations
**Usage**: Monitor workflow progress and identify optimization opportunities
```python
progress = evaluate_goal_progress(
    goal_id="aiGI_workflow_001",
    include_recommendations=True
)
```

### update_goal(goal_id, status, priority, progress, notes)
**Purpose**: Update goal parameters and status based on workflow progress
**Usage**: Maintain accurate goal tracking and status reporting
```python
update_goal(
    goal_id="aiGI_workflow_001",
    status="active",
    progress=0.75,
    notes="Workflow proceeding ahead of schedule"
)
```

## Strategy Management Tools

### select_optimal_strategy(context, constraints, objectives)
**Purpose**: Select best strategy for given context and constraints
**Usage**: Choose optimal workflow approach based on project requirements
```python
strategy = select_optimal_strategy(
    context="aiGI_development",
    constraints={
        "time_limit": 3600,
        "memory_limit": "8GB",
        "cpu_cores": 4
    },
    objectives=["performance", "quality", "maintainability"]
)
```

### list_strategies(context_filter, effectiveness_threshold)
**Purpose**: List available strategies with effectiveness metrics
**Usage**: Review available strategies and their performance history
```python
strategies = list_strategies(
    context_filter="development",
    effectiveness_threshold=0.8
)
```

### create_custom_strategy(strategy_name, description, context, steps, expected_outcomes)
**Purpose**: Create new strategies based on workflow experience
**Usage**: Develop custom strategies for specific project requirements
```python
create_custom_strategy(
    strategy_name="rapid_prototyping",
    description="Fast iteration strategy for proof-of-concept development",
    context="prototyping",
    steps=[
        "minimal_viable_implementation",
        "rapid_testing",
        "quick_feedback_integration"
    ],
    expected_outcomes=["fast_delivery", "early_validation", "risk_reduction"]
)
```

### evaluate_strategy_performance(strategy_id, evaluation_period_hours, metrics)
**Purpose**: Assess strategy effectiveness over time
**Usage**: Analyze strategy performance for continuous improvement
```python
performance = evaluate_strategy_performance(
    strategy_id="performance_optimization_001",
    evaluation_period_hours=168,
    metrics=["success_rate", "completion_time", "quality_score"]
)
```

## Learning and Adaptation Tools

### trigger_learning_cycle(learning_type, data_sources, focus_areas)
**Purpose**: Initiate adaptive learning process for continuous improvement
**Usage**: Trigger learning when performance degrades or new patterns emerge
```python
trigger_learning_cycle(
    learning_type="incremental",
    data_sources=["workflow_metrics", "error_patterns", "performance_data"],
    focus_areas=["optimization", "error_reduction", "quality_improvement"]
)
```

### get_learning_metrics(metric_type, time_range_hours)
**Purpose**: Retrieve learning performance metrics and trends
**Usage**: Monitor learning effectiveness and adaptation progress
```python
metrics = get_learning_metrics(
    metric_type="all",
    time_range_hours=24
)
```

### update_learning_parameters(learning_rate, adaptation_threshold, memory_retention, exploration_factor)
**Purpose**: Modify learning configuration for optimal adaptation
**Usage**: Tune learning parameters based on workflow performance
```python
update_learning_parameters(
    learning_rate=0.15,
    adaptation_threshold=0.1,
    memory_retention=0.9,
    exploration_factor=0.2
)
```

### analyze_adaptation_patterns(pattern_type, analysis_depth, time_window_days)
**Purpose**: Analyze system adaptation trends and behavioral evolution
**Usage**: Understand adaptation patterns for strategic planning
```python
patterns = analyze_adaptation_patterns(
    pattern_type="all",
    analysis_depth="comprehensive",
    time_window_days=7
)
```

## Agent Management Tools

### create_agent_session(agent_type, session_config, timeout_seconds)
**Purpose**: Create new agent interaction session for specialized tasks
**Usage**: Spawn specialized agents for distributed processing
```python
session_id = create_agent_session(
    agent_type="cognitive",
    session_config={
        "focus": "code_analysis",
        "depth": "comprehensive",
        "quality_threshold": 0.8
    },
    timeout_seconds=3600
)
```

### interact_with_agent(session_id, command, parameters, async_mode)
**Purpose**: Send commands or queries to agent sessions
**Usage**: Coordinate agent activities and task delegation
```python
result = interact_with_agent(
    session_id="cognitive_001",
    command="analyze_code_quality",
    parameters={
        "file_path": "responses_LS1.md",
        "analysis_depth": "comprehensive"
    },
    async_mode=False
)
```

### list_agent_sessions(filter_by_type, include_inactive)
**Purpose**: List active agent sessions for monitoring and coordination
**Usage**: Monitor agent status and coordinate multi-agent workflows
```python
sessions = list_agent_sessions(
    filter_by_type="cognitive",
    include_inactive=False
)
```

### terminate_agent_session(session_id, force)
**Purpose**: Terminate agent sessions and cleanup resources
**Usage**: Manage agent lifecycle and resource cleanup
```python
terminate_agent_session(
    session_id="cognitive_001",
    force=False
)
```

## System Monitoring Tools

### monitor_system_health(check_interval, alert_thresholds)
**Purpose**: Monitor SAFLA system health and generate alerts
**Usage**: Continuous system monitoring and proactive issue detection
```python
monitor_system_health(
    check_interval=30,
    alert_thresholds={
        "cpu_usage": 0.8,
        "memory_usage": 0.85,
        "error_rate": 0.05
    }
)
```

### get_system_info()
**Purpose**: Get comprehensive SAFLA system information and status
**Usage**: Assess system capabilities and current state
```python
system_info = get_system_info()
# Returns: version, capabilities, resource_usage, performance_metrics
```

## Tool Usage Patterns

### Workflow Initialization
```python
# 1. Get current system state
awareness = get_system_awareness()
system_info = get_system_info()

# 2. Create project goals
create_goal(
    goal_name="project_completion",
    description="Complete aiGI workflow with optimal performance",
    priority="high"
)

# 3. Select optimal strategy
strategy = select_optimal_strategy(
    context="aiGI_development",
    constraints=system_info["constraints"],
    objectives=["performance", "quality"]
)
```

### Adaptive Learning Integration
```python
# Monitor progress and trigger learning when needed
progress = evaluate_goal_progress("project_001")
if progress["delta"] < 0.1:  # Δ < ε threshold
    trigger_learning_cycle(
        learning_type="incremental",
        focus_areas=["optimization", "error_reduction"]
    )
```

### Agent Coordination
```python
# Create specialized agents for distributed processing
cognitive_agent = create_agent_session("cognitive", {"focus": "analysis"})
memory_agent = create_agent_session("memory", {"focus": "optimization"})

# Coordinate agent activities
interact_with_agent(cognitive_agent, "analyze_workflow")
interact_with_agent(memory_agent, "optimize_vectors")