Adobe Learning Manager (ALM) MCP Server

Wraps the ALM Prime API v2 so an LLM client can look up learners, browse learning objects, manage enrollments, check badges, and kick off async jobs (CSV exports, certificate generation).

Reference docs: https://learningmanager.adobe.com/docs/primeapi/v2/

Auth: you need an OAuth access token first

ALM's Prime API uses OAuth 2.0, not a static API key. Before this server is useful you need:

1. An ALM integration registered (Admin > Integrations in ALM, or via your Adobe admin console) to get a client ID/secret.

2. An OAuth access token obtained through ALM's auth flow using that client ID/secret.

This server takes a already-issued access token via ALM_ACCESS_TOKEN as a fallback default — but if multiple people connect to this server (the point of hosting it remotely), each person should call set_access_token with their own token instead of relying on the shared default. See "Multi-user auth model" below for why. This server does not handle the OAuth exchange or refresh itself — for long-lived use, either:

• refresh the token externally and call set_access_token again, or

• extend _get_token() / add a token-refresh helper if you have refresh tokens available.

Requests are sent as Authorization: oauth <token> (ALM's documented header format) with JSON:API content types.

Related MCP server: OpenProject MCP Server

Multi-user auth model (read this before deploying for a team)

This server is designed to be safely used by multiple people connected at once — e.g. several Adobe employees, each with their own ALM identity. Tokens are stored per MCP session, not globally:

• Each connecting client gets its own isolated token once they call set_access_token.

• One person's token can never leak into or affect another person's requests, even if both are using the server at the same time.

• If a session hasn't called set_access_token yet, it falls back to the server's ALM_ACCESS_TOKEN env var (if set) — fine for solo testing, but every real user should set their own token rather than share that default once this is used by more than one person.

This is still simpler than full OAuth: there's no real login flow, and nothing stops someone from pasting in a token that isn't theirs. The proper next step is a real OAuth Resource Server backed by Adobe IMS (TokenVerifier + AuthSettings from mcp.server.auth), where each person authenticates via an actual login redirect instead of manually copying a token. That requires registering an OAuth client in Adobe IMS's console — an Adobe-internal identity/IT action — so it's a deliberately separate, bigger step from what's implemented here.

Setup

cd alm-mcp-server
uv venv
source .venv/bin/activate
uv sync

Tools included

Users

• list_users(page_limit, page_offset, filter_field, filter_value)

• get_user(user_id)

• create_user(name, email, user_type, user_unique_id)

• update_user(user_id, attributes)

• delete_user(user_id)

• list_user_groups(user_id, page_limit)

Learning objects & catalogs

• list_learning_objects(lo_types, page_limit)

• get_learning_object(lo_id, include)

• list_catalogs(page_limit)

Enrollments

• list_enrollments(user_id, lo_types, sort)

• enroll_user(user_id, lo_id, lo_instance_id, allow_multi_enrollment)

• get_enrollment(user_id, enrollment_id, include)

• unenroll_user(user_id, enrollment_id)

Badges

• list_user_badges(user_id)

Jobs (async)

• create_job(job_type, job_params)

• get_job_status(job_id)

Auth maintenance

• set_access_token(new_token) — set your own OAuth token for your session only, without restarting the server. Use this when you first connect, or when you hit a 401 "Token expired" error. Isolated per-session — does not affect or get affected by any other connected user.

Test it

npx @modelcontextprotocol/inspector python server.py

Try list_users with a small page_limit first to confirm auth works before attempting writes.

Connect to Claude Desktop

{
  "mcpServers": {
    "adobe_learning_manager": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/alm-mcp-server",
        "run",
        "server.py"
      ],
      "env": {
        "ALM_BASE_URL": "https://learningmanager.adobe.com",
        "ALM_ACCESS_TOKEN": "your-oauth-access-token"
      }
    }
  }
}

Use https://learningmanagereu.adobe.com (or your account's assigned region) instead if your ALM tenant is EU-hosted.

Known gotcha: eventual consistency on create-then-enroll

If you call create_user and immediately call enroll_user for that same user, ALM's enrollment lookup can briefly fail with "object doesn't exist" — the new user hasn't finished indexing yet. If you hit this, add a short delay (a few seconds) and retry once or twice with backoff before treating it as a real error.

Scope note

This server is read-heavy for learning objects and read/write for users + enrollments, which matches how the API is actually used in practice — ALM's write API is oriented around learners and their relationship to content (enroll, track progress) rather than authoring course content itself, which is typically done through the ALM UI.

Before production use

• Scope the OAuth integration's permissions as narrowly as ALM allows (e.g. a service account without full admin rights) rather than using a personal admin token.

• Treat delete_user and unenroll_user as destructive — consider requiring the caller to have just fetched the record via get_user / get_enrollment before deleting, so the model is acting on a confirmed ID rather than a guessed one.

• Add retry/backoff for the eventual-consistency gotcha above if you automate create+enroll flows.