Provides an adapter layer (fastapimcp) that automatically registers FastAPI HTTP endpoints as MCP tools, enabling educational domain services like grading, practice planning, cognitive diagnosis, knowledge tracing, and sentiment analysis to be called by AI agents.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Educational MCP Serverdiagnose student understanding of algebra based on recent quiz results"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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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