"""
Concept-related MCP tools for TutorX.
"""
import random
from typing import Dict, Any, Optional
from datetime import datetime, timezone
import sys
import os
from pathlib import Path
import json

# Add the parent directory to the Python path
current_dir = Path(__file__).parent
parent_dir = current_dir.parent.parent
sys.path.insert(0, str(parent_dir))

import sys
import os
from pathlib import Path

# Add the parent directory to the Python path
current_dir = Path(__file__).parent
parent_dir = current_dir.parent
sys.path.insert(0, str(parent_dir))

# Import from local resources
from resources.concept_graph import get_concept, get_all_concepts

# Import MCP
from mcp_server.mcp_instance import mcp
from mcp_server.model.gemini_flash import GeminiFlash

MODEL = GeminiFlash()

@mcp.tool()
async def get_concept_tool(concept_id: str = None) -> dict:
    """
    Get a specific concept or all concepts from the knowledge graph, fully LLM-driven.
    If a concept_id is provided, use Gemini to generate a JSON object with explanation, key points, and example.
    """
    if not concept_id:
        return {"error": "concept_id is required for LLM-driven mode"}
    prompt = (
        f"Explain the concept '{concept_id}' in detail. "
        f"Return a JSON object with fields: explanation (string), key_points (list of strings), and example (string)."
    )
    llm_response = await MODEL.generate_text(prompt)
    try:
        data = json.loads(llm_response)
    except Exception:
        data = {"llm_raw": llm_response, "error": "Failed to parse LLM output as JSON"}
    return data

@mcp.tool()
async def assess_skill_tool(student_id: str, concept_id: str) -> dict:
    """
    Assess a student's understanding of a specific concept, fully LLM-driven.
    Use Gemini to generate a JSON object with a score (0-1), feedback, and recommendations.
    """
    prompt = (
        f"A student (ID: {student_id}) is being assessed on the concept '{concept_id}'. "
        f"Generate a JSON object with: score (float 0-1), feedback (string), and recommendations (list of strings)."
    )
    llm_response = await MODEL.generate_text(prompt)
    try:
        data = json.loads(llm_response)
    except Exception:
        data = {"llm_raw": llm_response, "error": "Failed to parse LLM output as JSON"}
    return data