Educational MCP Server

Educational MCP Server

Enables AI assistants to access educational domain tools including grading, cognitive diagnosis, knowledge tracing, learning path recommendations, and sentiment analysis through FastAPI-powered small models.

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README

AI 教学智能体

  • LLM 负责理解自然语言与决策；所有“教育小模型”通过 FastAPI 暴露，接口统一。
  • 状态（掌握度/情绪/作答历史）用轻量内存存储演示，后续可替换数据库（SQLAlchemy 等）。

目录结构

main.py            # FastAPI 入口，挂载所有路由
schemas.py         # Pydantic 数据模型（请求/响应）
database.py        # 轻量内存“数据库”（可换持久化）
models/            # 小模型业务逻辑（可换训练模型）
  cognitive_diagnosis.py
  knowledge_tracking.py
  emotion_analysis.py
  path_planning.py
routers/           # FastAPI 路由拆分
  cognitive.py
  tracking.py
  emotion.py
  planning.py

接口一览

  • POST /diagnose：认知诊断（规则占位，可换 CDM/IRT）。
  • POST /track：知识追踪（规则占位，可换 DKVMN/AKT）。
  • POST /emotion，/emotion/sentiment：情感状态与文本情感。
  • POST /plan：路径规划。

所有请求支持 request_id；响应带 mode/model_version，标注实现可靠度。

启动

pip install -r requirements.txt
python main.py

默认监听 http://127.0.0.1:8000，访问 /docs 可在线调试。LLM 侧可直接把这些 HTTP 路由注册为 MCP 工具。

演进建议

  • 用持久化数据库替换 database.py，保留同名接口即可平滑升级。
  • 将 models/* 内的规则逻辑替换为训练模型；对外 schema 不变。

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