learn-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LEARN_MCP_DB | No | Override the path to the SQLite database file. Defaults to ~/.learn-mcp/learn.sqlite |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_problemA | Store an agent-authored, LeetCode-style DSA problem and return its id/slug. YOU (the agent) write the creative, immersive content; this tool persists it with structure so it can be practiced in a session. For multi-step problems, provide |
| start_sessionB | Begin a practice session for a stored problem. Returns the solver-facing problem (answers hidden) and a sessionId to use for hints, submissions, and steps. |
| get_hintA | Advance the hint escalation for a session. Returns the level and guidance on HOW deep a hint to give at this level — YOU write the actual hint, grounded in the problem's referenceApproach. Levels run 1 (nudge) to 4 (near-solution). |
| explain_conceptB | Record that a concept was taught during a session (keeps the flow's timeline honest) and echo the concept back. YOU write the explanation. Optionally tie it to a session for progress tracking. |
| submit_solutionA | Record a solution attempt. In v1 judging is agent-side: YOU evaluate the code against the examples + referenceApproach and pass your verdict ('pass'/'fail') plus feedback. A 'pass' marks the session solved. |
| next_stepB | Advance a multi-step problem to its next stage and return that step's prompt (its referenceApproach stays hidden). Errors if the problem is single-step or already at the last step. |
| progressA | Single-user practice stats across all sessions: solved counts by difficulty, and per-topic attempted/solved (use to surface weak areas). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| author_problem | Rubric the agent can pull in before generating a problem, so generated problems are immersive, well-formed, and correctly calibrated. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool targets a distinct action in the learning workflow—explain, create, hint, step, progress, session, submit—with no overlap.
All tool names follow a consistent verb_noun snake_case pattern (e.g., explain_concept, start_session).
Seven tools is well-scoped for a learning MCP server, covering concept recording, problem generation, session management, hints, and progress tracking.
The core learning loop is covered, but there is no tool to list or browse existing problems, which agents might need to select a problem for a session.