ohneben's LearnWorlds MCP

Run your LearnWorlds school in plain language from AI assistants like Claude, Cursor, and any other MCP client.

This Model Context Protocol server exposes the LearnWorlds public API — all 94 endpoints, generated straight from the OpenAPI spec into MCP tools. Every tool is safety-categorized (🟢 read-only / 🟡 write / 🔴 destructive) so your assistant knows what an action does before it calls it. It runs over stdio (Claude Desktop and other local launchers) or Streamable HTTP (hosted in Docker), and ships with retries, client-side rate limiting, and request timeouts so it holds up against a live school.

Why you'll want this

Some MCP servers just forward an API. This one is built to be safe to hand to an LLM and easy to run for real:

How it compares

At the time of writing this appears to be the only dedicated LearnWorlds MCP server. You could instead point a generic OpenAPI→MCP wrapper at the spec — here's what that leaves on the table:

_{*Generic OpenAPI→MCP wrappers turn any Swagger/OpenAPI spec into MCP tools. They can reach the same endpoints, but treat every operation identically — no safety categories, no resilience, no deployment story, and no guardrails tuned for live school data. "➖" = varies by tool / not guaranteed. Snapshot from July 2026.}

What you can do

Once it's connected, ask your assistant things like:

• "List the 10 most recent users who signed up this month."
• "Create a user for jane@example.com and enroll her in the 'Pro' bundle."
• "Show me this month's payments and total revenue."
• "Which users haven't completed the 'Onboarding' course yet?"
• "Create a 20%-off coupon for the annual subscription plan."
• "Pull completion analytics for our top 5 courses."

Tools are generated automatically from the API and grouped into 🟢 read-only, 🟡 write, and 🔴 destructive — so a well-behaved host can treat each group differently.

How it works

Claude / Cursor / any MCP client  ──MCP──►  this server  ──HTTPS──►  LearnWorlds API (your school)

The server parses the bundled OpenAPI spec into MCP tools (resolving $refs and guarding against recursive schemas), tags each with its safety category, and injects your bearer token and Lw-Client header on every outgoing request. Your credentials stay in the server's environment — the model never sees or handles them.

Requirements

• A LearnWorlds school with API access — an access token and a Client ID (admin → Settings → Integrations → Developers), plus your school's API base URL. See Get your API credentials.
• Docker (Docker Desktop on macOS/Windows) for the quick start below — or Node.js ≥ 18 to run from source.

Quick start (Docker)

1. Add your credentials. Copy the example config and fill it in:

cp .env.example .env
# edit .env → set LEARNWORLDS_BASE_URL, LEARNWORLDS_API_TOKEN, LEARNWORLDS_CLIENT_ID
#           → set MCP_SHARED_TOKEN to a long random string if reachable beyond localhost

2. Start the server:

docker compose up -d --build

The bundled docker-compose.yml binds to 127.0.0.1:8765 only, so the server is reachable from your machine but not the network.

3. Confirm it's running:

curl -s http://localhost:8765/health
# → {"status":"ok","server":"learnworlds-mcp","tools":94}

4. Connect your MCP client. The MCP endpoint is http://localhost:8765/mcp.

• Claude Desktop — add a custom connector (Settings → Connectors) pointing at the URL, or bridge it locally with mcp-remote. Add this under mcpServers in your config, then fully quit and reopen the app:

  config.json

  Copy

  {
    "mcpServers": {
      "learnworlds": {
        "command": "npx",
        "args": [
          "mcp-remote",
          "http://localhost:8765/mcp",
          "--header", "Authorization: Bearer YOUR_MCP_SHARED_TOKEN"
        ]
      }
    }
  }
  

  (Drop the --header line if you left MCP_SHARED_TOKEN empty.)

• Claude Code — one command:

  Terminal

  Copy

  claude mcp add --transport http learnworlds http://localhost:8765/mcp
  

• Claude Cowork — shares Claude Code's MCP config, so the command above makes the tools available there too.

Prefer a prebuilt image?

Every push to main publishes a ready-to-run image to the GitHub Container Registry, so you can skip the local build entirely:

docker run -d --name learnworlds-mcp -p 127.0.0.1:8765:8765 --env-file .env \
  ghcr.io/ohneben/learnworlds-mcp:latest

Find it in a registry

The server publishes itself to the official MCP Registry as io.github.ohneben/learnworlds-mcp, so MCP clients that browse the registry can install it without cloning anything:

curl "https://registry.modelcontextprotocol.io/v0/servers?search=io.github.ohneben/learnworlds-mcp"

server.json holds the registry metadata and glama.json the Glama directory entry.

Releasing is driven by the version in server.json: when a build sees a version the registry does not yet list, it tags the image with that version and publishes it, using GitHub OIDC so there is no token to store. Anything else — an ordinary push, a re-run, a second event for the same ref — leaves the released image tag and the registry entry untouched. Bumping the version is therefore the whole release: pushing a matching v* tag or running the workflow by hand still works and takes the same path, and a tag that disagrees with server.json fails the build rather than publishing the wrong thing.

The io.modelcontextprotocol.server.name label in the Dockerfile is what proves the image belongs to that name, so keep it in step with name in server.json. It is applied at build time — there is nothing to verify by hand.

Get your API credentials