dataflowr MCP Server

List all modules in the dataflowr Deep Learning DIY course. Can be filtered by session number, tag, or GPU requirement. Returns module IDs, titles, descriptions, and tags.

Get full details for a specific module. Includes description, notebooks with GitHub and Colab links, tags, and prerequisites. Use this when a student asks about a specific topic or module. Module IDs: '12' (Attention/Transformers), '2a' (PyTorch tensors), '18b' (diffusion), etc.

Search modules by keyword across titles, descriptions, and tags. Use this to find relevant modules for a student's question or topic. E.g. query='attention' finds the Transformer module; 'generative' finds GANs, autoencoders, flows, diffusion.

List all course sessions with their titles and module IDs. Sessions group modules into ~2-3 hour teaching blocks.

Get full details for a session, including all its modules, notebooks, and key takeaways. Use this when a student wants to understand what a session covers.

Start a tutoring session for a specific module. The agent fetches the module content, checks prerequisites, and explains key concepts step-by-step using the Socratic method.

Start an interactive quiz session for a module. The agent presents one question at a time, waits for the student's answer, then gives feedback before moving to the next question.

Help a student debug their code for a module's practical notebook. The agent reads the exercises and uses Socratic questioning to guide the student toward the solution without giving it away.

Build a personalised learning path toward a target module. The agent walks the full prerequisite chain and orders the modules the student still needs to cover. known_modules: comma-separated IDs of modules already completed (optional)