Allows EduSmart AI Tutor to use OpenAI models for explaining concepts, generating novel questions, and grading free-text conceptual answers.
Click on "Deploy 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., "@EduSmart AI Tutor MCP Server Help me practice solving quadratic equations and suggest what to study next."
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.
An AI-powered personalized learning platform exposed through a Model Context Protocol (MCP) server, so an MCP-compatible AI client (Claude, ChatGPT Apps SDK, or any MCP host) can tutor a student through a real learning-workflow platform — knowledge retrieval, assessment, mastery tracking, and personalized recommendations — not a generic chatbot with a system prompt.
Student → AI Tutor → Knowledge → Assessment → Mastery → Next Action → Demonstrated CompetenceA relational learner model (PostgreSQL/SQLite): students, courses, a skill prerequisite graph, mastery, attempts, assessments, projects, and an append-only learning history.
A real RAG pipeline: document ingestion → chunking → embeddings → a FAISS vector index → a retriever that joins back to source metadata for attribution.
A deterministic-first learning engine: mastery calculation, prerequisite resolution, and
the get_next_learning_action recommendation are pure functions — no LLM call decides them.
An LLM provider abstraction (stub / OpenAI / Anthropic) used only where language generation genuinely helps: explaining a concept, generating novel questions, and grading free-text conceptual answers.
A LangGraph tutor workflow that routes a request through explicit nodes (classify intent → load student state → teach/practice/assessment/project → update learning state → recommend next step) instead of an unconstrained agent loop.
A 17-tool MCP server (app/mcp/server.py) plus an equivalent REST API (app/main.py),
both thin adapters over the same authorization-enforcing service layer.
Related MCP server: Knowledge Graph MCP Server
59 automated tests, all passing, across unit, integration, MCP, RAG, and evaluation suites —
run python -m pytest tests/ -q to verify. See docs/DEVELOPMENT.md for a
phase-by-phase IMPLEMENTED/TESTED/PARTIALLY-IMPLEMENTED breakdown (Docker was written but not
executed in this build environment; everything else has been run for real, not just written).
python -m venv .venv && source .venv/Scripts/activate # or .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env
python -m alembic upgrade head
python scripts/seed_data.py
python scripts/ingest_documents.py
uvicorn app.main:app --reload & # REST API on :8000
python scripts/run_mcp_server.py & # MCP server on :8765
python -m pytest tests/ -q # 59 passedFull setup, every command explained, and known pitfalls: docs/DEVELOPMENT.md.
Doc | Covers |
System diagram, layering, deterministic-vs-LLM decisions, technology inventory | |
The 17 MCP tools, authentication approach, security boundaries, ChatGPT integration readiness | |
Ingestion → chunking → embeddings → FAISS → retrieval, and why each part is swappable | |
Schema (24 tables), ER diagram, PostgreSQL/SQLite portability | |
Authn/authz model, prompt-injection defenses, what's out of scope | |
Real measured retrieval/grading/mastery-model results | |
Docker Compose, configuration, production gaps | |
REST endpoint reference | |
Full local setup, test suite layout, phase-by-phase status |
MIT — see LICENSE.
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