notebooklm

NotebookLM via MCP

This skill uses the @m4ykeldev/notebooklm-mcp server, which exposes NotebookLM as 27 MCP tools. NotebookLM has no public API; the server talks to the same internal RPC endpoint the web app uses, authenticated with the user's own Google session cookies.

Setup

Check first: if tools such as notebook_list are already available, skip this section.

1. Authenticate once (opens Chrome, waits for the Google login, stores cookies in ~/.notebooklm-mcp/auth.json):

   npx -y @m4ykeldev/notebooklm-mcp auth
   

   Headless / CI: set NOTEBOOKLM_COOKIES instead. Fallbacks: auth --manual (paste cookies) or auth --file tokens.json.

2. Register the server with the client.

   Claude Code:

   claude mcp add --transport stdio notebooklm -- npx -y @m4ykeldev/notebooklm-mcp serve
   

   Any JSON-configured client (Cursor, VS Code, Claude Desktop, ...):

   {
     "mcpServers": {
       "notebooklm": {
         "command": "npx",
         "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
       }
     }
   }
   

   Long answers on big notebooks: append "--query-timeout", "180000" to args.

The authentication step needs the user at a browser. Ask them to run it; do not try to log in on their behalf.

Core workflow

1. Find or create the notebook. notebook_list returns IDs, titles and source counts. Match by title before creating a duplicate. notebook_create returns the new ID.
2. Add sources.
   • notebook_add_url — web pages and YouTube links.
   • notebook_add_text — pass content inline, or file_path for a local file. file_path must sit under the server's working directory or the OS temp dir; for anything else, read the file and pass content.
   • notebook_add_drive — needs the Drive file_id, a title and the MIME doc_type (e.g. application/vnd.google-apps.document).
   • Sources take a moment to process. Check with notebook_get before querying a source you just added.
3. Ask questions. notebook_query answers only from the notebook's sources, using all of them by default. Restrict with source_ids when the user cares about specific documents. For follow-ups, pass back the returned conversation_id so NotebookLM keeps context.
4. Produce output with the Studio tools (below), or summarise with notebook_describe.