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 |
Capabilities
Features and capabilities supported by this server
| 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 | |
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