NotebookLM

Overview

NotebookLM answers questions only from the sources uploaded to a notebook, with inline citations to the exact passages used — no open-web knowledge, so answers are hallucination-resistant and fully traceable. This skill drives the @roomi-fields/notebooklm-mcp engine to query notebooks, manage sources, and generate Studio content, and encodes the patterns that make NotebookLM usable at research scale (citation formats, the ~50-queries/day quota, batch-to-cache).

Choosing the transport

Two ways reach the same engine — pick per what the session already has:

1. notebooklm MCP tools — if tools such as notebook_ask / source_add / server_health (or mcp__notebooklm__*) are available in the session, call them directly. This is the preferred path and needs no server.
2. HTTP REST API — otherwise, use the bundled scripts/nblm.sh, which talks to a running NotebookLM MCP server (default http://localhost:3000, override with NOTEBOOKLM_SERVER_URL). If no server is reachable, ask the user to start one (npm run start:http from a clone) or to install the MCP.

Both are backed by the same account and session, so the choice is purely about which is already wired up.

Prerequisite: one Google login

NotebookLM needs a signed-in Google session (saved once, reused across runs). Verify with nblm.sh health (or the server_health tool) — look for authenticated: true. If not authenticated, run the interactive login in a terminal (a visible Chrome window opens):

notebooklm-mcp-setup-auth          # global install
# or:  scripts/nblm.sh auth

Run the login in a terminal rather than through an in-client tool: interactive Google login can take minutes and a stdio client's tool-call timeout may cut it off.

Core tasks

Use scripts/nblm.sh for the REST path (or the equivalent MCP tool):

scripts/nblm.sh health                       # reachability + auth status
scripts/nblm.sh notebooks                     # list notebooks (id + name)
scripts/nblm.sh ask "<question>" <notebook_id>   # citation-backed answer (JSON citations)
scripts/nblm.sh generate <notebook_id> report   # audio|report|video|infographic|presentation|data_table|flashcards|quiz|mind_map

• Ask: the script requests source_format: json, so the answer carries source names + cited excerpts. For a human-facing answer, prefer expanded (see references/rest-api.md to vary the format).
• Generate: flashcards/quiz route to the study-aid endpoint and mind_map to the mind-map endpoint automatically.

Working effectively (read before large runs)

For anything beyond a few questions, load references/research-workflows.md. Key points:

• Quota: free accounts cap at ~50 chat queries/day. Rotate accounts (/re-auth) or, better, ingest once and retrieve offline.
• Batch → cache: for literature reviews / SOTA surveys, run an exhaustive question set through /batch-to-vault (writes markdown + nblm-answer-v1 JSON sidecars with citations), then answer repeated questions from the cache (e.g. with RTFM) — unlimited, offline.
• Fresh vs. follow-up: omit session_id for independent questions (fastest); pass a stable one to continue a conversation.

References

• references/rest-api.md — endpoint + body reference for the HTTP path.
• references/research-workflows.md — citation formats, quota strategy, the batch/ingestion pattern, source discovery.

Installing the engine

If neither the MCP tools nor a server are present, the engine is the npm package @roomi-fields/notebooklm-mcp (also a Claude Code plugin via the roomi-fields/claude-plugins marketplace). Point the user there, then run the one-time login above.