"""
AGI Tool - Mind Engine (Δ) MCP Interface
v52.6.0 - Logic, truth, and clarity

Wraps codebase AGI engine for MCP consumption.
"""

from typing import Any, Dict, Optional


class AGITool:
    """
    Mind Engine: SENSE → THINK → ATLAS

    Role: Logical reasoning, truth validation, clarity enforcement
    """

    @staticmethod
    def execute(action: str, text: str, session_id: Optional[str] = None, **kwargs) -> Dict[str, Any]:
        """Execute AGI action"""

        if action == "sense":
            # Pattern recognition and input analysis
            return AGITool._sense(text, session_id, **kwargs)
        elif action == "think":
            # Deep logical reasoning
            return AGITool._think(text, session_id, **kwargs)
        elif action == "map":
            # Knowledge mapping (ATLAS)
            return AGITool._map(text, session_id, **kwargs)
        else:
            return {"verdict": "VOID", "reason": f"Unknown AGI action: {action}"}

    @staticmethod
    def _sense(text: str, session_id: Optional[str], **kwargs) -> Dict[str, Any]:
        """Analyze input for patterns and context"""

        # Simulate AGI sense evaluation
        # In production, calls codebase AGI engine

        violations = []

        # Check for injection patterns (F12)
        injection_keywords = ["ignore previous", "disregard", "new instructions", "forget"]
        if any(keyword in text.lower() for keyword in injection_keywords):
            violations.append("F12: Injection pattern detected")

        # Check for clarity (F4)
        if len(text.strip()) < 3:
            violations.append("F4: Input too vague (ΔS violation)")

        # Check for factual claims without sources (F2)
        confidence_words = ["definitely", "absolutely", "100%", "always", "never"]
        if any(word in text.lower() for word in confidence_words):
            violations.append("F2: Overconfident claim without source")

        if violations:
            return {
                "verdict": "VOID",
                "reason": "; ".join(violations),
                "clarity": 0.3,
                "truth_confidence": 0.5
            }

        return {
            "verdict": "SEAL",
            "clarity": 0.95,
            "truth_confidence": 0.99,
            "lane": "FACTUAL",
            "entropy_delta": -0.2  # Negative = clarity improved
        }

    @staticmethod
    def _think(text: str, session_id: Optional[str], **kwargs) -> Dict[str, Any]:
        """Perform deep logical reasoning"""

        # Simulate AGI thinking process
        reasoning_steps = [
            "Parse input structure",
            "Identify logical claims",
            "Check for contradictions",
            "Assess truth value"
        ]

        return {
            "verdict": "SEAL",
            "reasoning_steps": reasoning_steps,
            "logic_score": 0.92,
            "humility": 0.04  # Ω₀ ∈ [0.03, 0.05]
        }

    @staticmethod
    def _map(text: str, session_id: Optional[str], **kwargs) -> Dict[str, Any]:
        """Map knowledge boundaries (ATLAS)"""

        # Simulate ATLAS knowledge mapping
        known_domains = ["programming", "ethics", "AI governance"]
        unknown_domains = ["personal medical advice", "legal binding decisions"]

        return {
            "verdict": "SEAL",
            "known_domains": known_domains,
            "unknown_domains": unknown_domains,
            "ontology_lock": True,  # F10: Staying in lane
            "knowledge_boundary": 0.85
        }
