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Refactor TutorX MCP server to integrate Mistral OCR for document processing, update concept graph tools for LLM-driven responses, and enhance learning path generation with Gemini. Transitioned various tools to utilize LLM for improved educational interactions and streamlined API responses.
a806ca2
| """ | |
| Lesson generation tools for TutorX MCP. | |
| """ | |
| from typing import Dict, Any, List | |
| from mcp_server.mcp_instance import mcp | |
| from mcp_server.model.gemini_flash import GeminiFlash | |
| import json | |
| MODEL = GeminiFlash() | |
| async def generate_lesson_tool(topic: str, grade_level: int, duration_minutes: int) -> dict: | |
| """ | |
| Generate a lesson plan for the given topic, grade level, and duration, fully LLM-driven. | |
| Use Gemini to generate a JSON object with objectives, activities, materials, assessment, differentiation, and homework. | |
| """ | |
| prompt = ( | |
| f"Generate a detailed lesson plan as a JSON object for the topic '{topic}', grade {grade_level}, duration {duration_minutes} minutes. " | |
| f"Include fields: objectives (list), activities (list), materials (list), assessment (dict), differentiation (dict), and homework (dict)." | |
| ) | |
| 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 | |