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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
LEARN_MCP_DBNoOverride the path to the SQLite database file. Defaults to ~/.learn-mcp/learn.sqlite

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
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 steps. Provide a referenceApproach (hidden from the solver) to ground hints and judging.

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

NameDescription
author_problemRubric 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

NameDescription

No resources

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