learn-mcp

An MCP server for agent-guided DSA practice. Connect it to an IDE agent (Claude Code, Cursor, etc.) and instead of static LeetCode problems, the agent generates immersive, LeetCode-style problems on demand and tutors you through them — escalating hints, concept explanations, and multi-step think-throughs.

The server provides structure and memory (problems, sessions, hint escalation, progress); the connected agent provides the creativity and teaching. No LLM runs inside the server.

Install

Install once from GitHub — it builds itself on install and exposes a learn-mcp command:

npm install -g github:abp2204/learn-mcp

Then register it with any MCP-capable agent. Because it's a global command, the config is the same in every folder — and so is your progress (one store in your home dir).

Claude Code:

claude mcp add learn-mcp -- learn-mcp

Cursor / Claude Desktop / any client (mcpServers config):

{
  "mcpServers": {
    "learn-mcp": { "command": "learn-mcp" }
  }
}

Prefer not to install globally? Use npx instead — same effect, nothing installed:

{
  "mcpServers": {
    "learn-mcp": { "command": "npx", "args": ["-y", "github:abp2204/learn-mcp"] }
  }
}

Now open any project in your agent and say "generate me a medium graph problem" — it works regardless of which folder you're in.

From source (development)

git clone https://github.com/abp2204/learn-mcp && cd learn-mcp
npm install      # builds via the prepare script
npm run dev      # run from TS source (stdio)
npm run inspect  # explore the tools in the MCP Inspector

Related MCP server: LeetCode MCP Server

How a session goes

1. You ask the agent for, say, a medium graph problem with a story.

2. Agent authors it and calls generate_problem → it's stored with a stable id.

3. start_session drops you into the problem (answers hidden).

4. Stuck? get_hint escalates 1 → 4 (nudge → near-solution); the server tracks the level so hints don't over-reveal. explain_concept teaches an underlying idea. next_step advances multi-step problems.

5. submit_solution records your attempt; a pass marks it solved.

6. progress shows what you've solved and which topics are weak.

Tools

Plus an author_problem MCP prompt: a rubric the agent can pull in to write well-calibrated, immersive problems.

Storage

One global SQLite file in your home dir — ~/.learn-mcp/learn.sqlite (respects XDG_DATA_HOME; override with the LEARN_MCP_DB env var). This is why your problems and progress are shared across every folder and agent, and survive restarts. Uses Node's built-in node:sqlite, so there's no native build step. The server prints the active store path to stderr on startup.

Status

v1. Solutions are agent-judged (the agent evaluates your code and reports pass/fail). A sandboxed code executor with generated test cases is the planned next step. Domain is DSA; DSP is a parked future idea.

Development

See CLAUDE.md for architecture and conventions. Run the end-to-end test with:

node scripts/smoke.mjs

Requires Node 22.5+ (uses built-in node:sqlite).