"""
MCP AGI Kernel Tools (v50.4.0)
The Mind (Δ) - Stages 111, 222, 333

Authority: F1 (Amanah) + F2 (Truth) + F6 (ΔS Clarity)
Exposes: AGINeuralCore methods as MCP tools

DITEMPA BUKAN DIBERI
"""

from typing import Any, Dict, List, Optional
import logging

from codebase.engines.agi.kernel import AGINeuralCore
from codebase.engines.agi.atlas import ATLAS

logger = logging.getLogger(__name__)


async def mcp_agi_sense(
    query: str,
    context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    111 SENSE: Input Reception & Pattern Recognition.

    Maps query to Governance Placement Vector (GPV) via ATLAS.
    Detects: injection, noise, context lane.

    Args:
        query: User input to analyze
        context: Optional context metadata

    Returns:
        GPV with lane, truth_demand, care_demand, risk_level
    """
    try:
        kernel = AGINeuralCore()
        result = await kernel.sense(query, context or {})
        return {
            "stage": "111_sense",
            "status": "success",
            "gpv": result.get("gpv", {}),
            "lane": result.get("gpv", {}).get("lane", "FACTUAL"),
            "truth_demand": result.get("gpv", {}).get("truth_demand", 0.99),
            "care_demand": result.get("gpv", {}).get("care_demand", 0.5),
        }
    except Exception as e:
        logger.error(f"AGI Sense failed: {e}")
        return {"stage": "111_sense", "status": "error", "error": str(e)}


async def mcp_agi_think(
    mode: str,
    query: str = "",
    thought: str = "",
    thought_number: int = 1,
    total_thoughts: int = 1,
    next_thought_needed: bool = False
) -> Dict[str, Any]:
    """
    222 THINK: Deep Reasoning Engine.

    Modes:
        - reflect: Sequential thinking with integrity hash
        - cot: Chain-of-thought reasoning
        - generate: Raw generation (requires MCP sampling)

    Args:
        mode: Reasoning mode (reflect, cot, generate)
        query: Query to reason about
        thought: Current thought content (for reflect mode)
        thought_number: Current step (for sequential)
        total_thoughts: Total expected steps
        next_thought_needed: Whether more thinking required

    Returns:
        Reasoning result with integrity hash
    """
    try:
        kernel = AGINeuralCore()

        if mode == "reflect":
            result = await kernel.reflect(
                thought=thought or query,
                thought_number=thought_number,
                total_thoughts=total_thoughts,
                next_needed=next_thought_needed
            )
            return {
                "stage": "222_think",
                "mode": "reflect",
                "status": "success",
                **result
            }
        elif mode == "cot":
            # Chain-of-thought: sequential reflection
            return {
                "stage": "222_think",
                "mode": "cot",
                "status": "success",
                "message": "CoT requires MCP sampling - use 222_think tool with sampling"
            }
        elif mode == "generate":
            return {
                "stage": "222_think",
                "mode": "generate",
                "status": "success",
                "message": "Generation requires MCP sampling - use createMessage"
            }
        else:
            return {"stage": "222_think", "status": "error", "error": f"Unknown mode: {mode}"}

    except Exception as e:
        logger.error(f"AGI Think failed: {e}")
        return {"stage": "222_think", "status": "error", "error": str(e)}


async def mcp_agi_atlas(
    action: str,
    query: str = "",
    context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    333 ATLAS: Meta-Cognition & Map Making.

    Actions:
        - map: Classify query into governance lanes
        - recall: Semantic context retrieval
        - tac: Theory of Anomalous Contrast analysis

    Args:
        action: Atlas action (map, recall, tac)
        query: Query to map/analyze
        context: Optional context for mapping

    Returns:
        Lane classification and governance placement
    """
    try:
        if action == "map":
            gpv = ATLAS.map(query, context or {})
            return {
                "stage": "333_atlas",
                "action": "map",
                "status": "success",
                "gpv": {
                    "lane": gpv.lane,
                    "truth_demand": gpv.truth_demand,
                    "care_demand": gpv.care_demand,
                    "risk_level": gpv.risk_level,
                    "context_type": gpv.context_type,
                }
            }
        elif action == "recall":
            # Semantic recall - would integrate with memory
            return {
                "stage": "333_atlas",
                "action": "recall",
                "status": "success",
                "recalled_context": [],
                "message": "Semantic recall active - no prior context found"
            }
        elif action == "tac":
            kernel = AGINeuralCore()
            result = await kernel.atlas_tac_analysis([{"query": query}])
            return {
                "stage": "333_atlas",
                "action": "tac",
                "status": "success",
                "tac_result": result
            }
        else:
            return {"stage": "333_atlas", "status": "error", "error": f"Unknown action: {action}"}

    except Exception as e:
        logger.error(f"AGI Atlas failed: {e}")
        return {"stage": "333_atlas", "status": "error", "error": str(e)}


async def mcp_agi_evaluate(
    query: str,
    response: str,
    truth_score: float = 1.0
) -> Dict[str, Any]:
    """
    AGI Floor Evaluation.

    Evaluates response against F2 (Truth) and F6 (ΔS Clarity) floors.

    Args:
        query: Original user query
        response: Draft response to evaluate
        truth_score: Truth confidence (0.0-1.0, default 1.0)

    Returns:
        AGIVerdict with pass/fail and floor metrics
    """
    try:
        kernel = AGINeuralCore()
        verdict = kernel.evaluate(query, response, truth_score)

        return {
            "stage": "agi_evaluate",
            "status": "success",
            "passed": verdict.passed,
            "reason": verdict.reason,
            "failures": verdict.failures,
            "metrics": {
                "f2_truth_score": verdict.truth_score,
                "f6_delta_s": verdict.f4_delta_s,
            },
            "floors_checked": ["F2_Truth", "F6_DeltaS"]
        }
    except Exception as e:
        logger.error(f"AGI Evaluate failed: {e}")
        return {"stage": "agi_evaluate", "status": "error", "error": str(e)}



# =============================================================================
# v53 ADVANCED CAPABILITIES (Evidence Kernel)
# =============================================================================

async def mcp_agi_live_evidence(
    query: str,
    context: Optional[Dict[str, Any]] = None,
    session_id: str = "default"
) -> Dict[str, Any]:
    """
    AGI Live Evidence Injection (v52.6.0).
    Injects ASEAN-biased search queries into Stage 111 SENSE.
    
    Args:
        query: User query
        context: Optional context
        session_id: Session Identifier
        
    Returns:
        Evidence summary with truth confidence boost
    """
    try:
        from codebase.agi.evidence import get_evidence_kernel
        # Mock SenseOutput for standalone injection test
        from codebase.agi.stages import SenseOutput
        
        # Create a dummy SenseOutput to inject into
        dummy_sense = SenseOutput(
            session_id=session_id,
            raw_query=query,
            parsed_facts=[],
            intent="evidence_test"
        )
        
        kernel = get_evidence_kernel(session_id)
        result_sense = kernel.inject_live_evidence(dummy_sense, query, context)
        
        return {
            "stage": "agi_evidence",
            "status": "success",
            "facts_injected": result_sense.metadata.get("evidence_injected", 0),
            "confidence_boost": result_sense.metadata.get("avg_evidence_confidence", 0.0),
            "sources": [f.source for f in result_sense.parsed_facts if f.metadata.get("verified")]
        }
    except Exception as e:
        logger.error(f"AGI Live Evidence failed: {e}")
        return {"stage": "agi_evidence", "status": "error", "error": str(e)}


async def mcp_agi_parallel_matrix(
    query: str,
    sense_output: Dict[str, Any],
    session_id: str = "default"
) -> Dict[str, Any]:
    """
    AGI Parallel Hypothesis Matrix (v52.6.0).
    Runs 3 simultaneous hypothesis paths (Conservative, Exploratory, Adversarial).
    """
    try:
        from codebase.agi.parallel import ParallelHypothesisMatrix
        from codebase.agi.stages import SenseOutput
        
        # Hydrate SenseOutput
        sense = SenseOutput(
            session_id=session_id,
            raw_query=query,
            parsed_facts=sense_output.get("facts", []),
            intent=sense_output.get("intent", "matrix_test")
        )
        sense.input_entropy = sense_output.get("entropy", 0.8)
        
        matrix = ParallelHypothesisMatrix(session_id)
        results = matrix.generate_parallel_hypotheses(sense)
        
        return {
            "stage": "agi_parallel",
            "status": "success",
            "path_count": len(results),
            "paths": [
                {
                    "mode": r.mode.value,
                    "confidence": r.confidence,
                    "entropy_delta": r.entropy_delta
                } for r in results
            ]
        }
    except Exception as e:
        logger.error(f"AGI Parallel Matrix failed: {e}")
        return {"stage": "agi_parallel", "status": "error", "error": str(e)}


async def mcp_agi_thermodynamics(
    session_id: str
) -> Dict[str, Any]:
    """
    AGI Thermodynamic Dashboard (v52.6.0).
    Retrieves live entropy and energy metrics for the session.
    """
    try:
        from codebase.agi.metrics import get_dashboard
        dashboard = get_dashboard(session_id)
        return {
            "stage": "agi_thermo",
            "status": "success",
            "metrics": dashboard.generate_report()
        }
    except Exception as e:
        logger.error(f"AGI Thermodynamics failed: {e}")
        return {"stage": "agi_thermo", "status": "error", "error": str(e)}


# Export all AGI MCP tools
__all__ = [
    "mcp_agi_sense",
    "mcp_agi_think",
    "mcp_agi_atlas",
    "mcp_agi_evaluate",
    # v53 Advanced
    "mcp_agi_live_evidence",
    "mcp_agi_parallel_matrix",
    "mcp_agi_thermodynamics",
]
