The MCP server is the small part; the work is the course agent that turns a docs repository into structured lessons, and you run it yourself against the repository you care about. The project describes itself as an experimental MVP, so expect to edit the generation script to point at your own repository and folders.
An experimental two-part system: a course content agent that reads a documentation repository and generates structured courses from it, and an MCP server that exposes those generated courses to an AI assistant so it can tutor from them.
- Courses generated from a documentation repository rather than written by hand — the pipeline runs docs through the course agent into structured course content
- Multi-complexity output: beginner, intermediate and advanced versions of the same material
- An MCP server that serves the generated course directory to a client, so the tutor answers from the course rather than from memory
- A documented Cursor setup where `@educational-tutor` brings the course tools into chat
Python with `uv`: create the environment (`python -m uv venv`), install in editable mode, generate courses, then start the server with `.venv/bin/uv run python -m mcp_server.main`. The course directory is chosen with the `COURSE_DIR` environment variable, and the documented client entry passes it explicitly. Course generation itself calls an LLM — the stack section names DSPy for orchestration and Gemini 2.5 Flash for generation — so that half needs whatever credentials your provider requires. The package is `mcp-educational-tutor` 0.1.0.