learning-mcp

An MCP server that teaches you a topic by enforcing a pedagogical workflow instead of suggesting one.

Ask a chat model to "teach me Kafka step by step" and it will agree, then quietly compress five steps into one. That isn't disobedience — it's what happens when the whole plan sits in context at once: the model can see step 5 while working step 1, so it hedges step 1 toward the finish and merges anything it judges redundant.

This server holds the steps instead. The next instruction does not exist in the model's context until it has handed back a valid artifact for the current one. Sequencing stops being a request and becomes a data dependency.

The workflow

research  ->  decompose  ->  drill (loop)  ->  done

1. Research — build a comprehensive, cited picture of the topic.

2. Decompose — break it into atomic elements, each with its prerequisites. The result is a DAG, not a list.

3. Drill — the loop that actually teaches. One element at a time, chosen by what's unlocked, due, and weakest.

Related MCP server: LearnFlow AI

What makes it a tutor rather than a quiz

Retrieval practice over re-reading. The default action is to ask, wait, and grade. Explanation is the fallback after a miss, not the main event.

Escalating demand. Each element moves recall → explain_back → apply as it becomes familiar. You have to produce the idea, not recognize it.

Prerequisites are enforced. An element is only drillable once everything it depends on is mastered, so you're never quizzed on consumer groups before partitions.

Interleaving comes free. Once several elements are unlocked, the scheduler alternates rather than drilling one to exhaustion.

Spacing, and a gate that resists cramming. Reviews follow an SM-2 interval ladder. Mastery additionally requires that one success landed a full day after a previous one — three right answers in a single sitting is short-term memory, and the gate says so.

The answer has to be yours. During a drill the server suspends mid-call to collect your answer, which reaches the model as a tool result it did not author. It cannot ask a question and answer it on your behalf.

Running it

Requires MCP SDK 2.0+ — the drill loop uses MCPServer and the Resolve/Elicit round trip, neither of which exists in the 1.x FastMCP API.

Locally (start here)

This is the right choice for almost everyone. The server runs as a subprocess of your client, your data stays on your disk, and there is nothing to secure.

pip install learning-mcp          # or: pip install git+https://github.com/ryantthomas/learning-mcp
claude mcp add learning -- learning-mcp

Then ask it to teach you something.

With Docker

docker build -t learning-mcp .
docker run -p 8000:8000 \
  -v learning-data:/data \
  -e LEARNING_MCP_TOKEN="$(openssl rand -hex 32)" \
  learning-mcp

The volume is not optional. Every topic, element and review date lives in one SQLite file under /data. Without a persistent volume the container starts empty every time, and you won't notice until the day you come back to review.

On your own cloud

The Dockerfile is the only deploy artifact, deliberately — it works on Fly, Railway, Render, Cloud Run, or any VPS, and locks you into none of them. Two things actually matter:

1. Attach a persistent volume and point LEARN_HOME at it (the image defaults to /data). Platforms with ephemeral filesystems will silently discard everything on restart.

2. Set LEARNING_MCP_TOKEN. The server refuses to start on a non-loopback interface without one. That's a deliberate fail-closed, not an obstacle to work around — an open URL is a read/write handle on your entire learning history.

$PORT is honoured, so most platforms need no further configuration.

Authentication, honestly

LEARNING_MCP_TOKEN enables Authorization: Bearer <token> on the HTTP transport. Configuration is per-instance, and each person runs their own — there is no multi-user mode and no notion of accounts.

That last row is worth reading twice if your goal is studying on your phone. Claude's custom connector settings expose Authorization URL, Token URL, Client ID and Client Secret — there is no field for a static token or custom header. A bearer-token server cannot be registered there. Making that work needs a real OAuth authorization server; the SDK supports it via auth_server_provider, but this project doesn't implement one yet.

Where your data lives

Everything is under ~/.learn (override with LEARN_HOME). SQLite is the source of truth; markdown is a projection of it, so a topic stays readable and greppable without the tool.

~/.learn/
  learn.db
  topics/kafka/
    research.md
    elements/01-partitions.md
    progress.md

A hosted instance moves this off your machine. The markdown mirror ends up on the server, where you can't grep or commit it. If those local files are the point for you, run stdio locally instead of deploying.

The schema is deliberately graph-shaped — prerequisites and concepts are their own tables, never JSON on a row — so a Neo4j projection later is an export rather than a rewrite. concepts is global while elements are topic-scoped, which is the seam that will let "partitioning" learned under Kafka count for itself again under Kinesis.

Design

The pedagogy lives in steps.py, mastery.py, and scheduler.py, none of which import mcp. That's deliberate: the teaching logic is a plain Python library that happens to be served over MCP, so it can be unit-tested without a model in the loop and re-fronted without a rewrite.

server.py is a thin adapter. Every advance requires the previous step's artifact as an argument — the model cannot obtain step N+1 without paying for step N.

Development

pip install -e ".[dev]"
pytest

The two tests worth knowing about, because they encode the whole point:

• the gate — a malformed artifact must not advance the phase

• concealment — a step's response must not contain any later step's text

License

MIT.