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notebooklm

Office & productivity

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Updated 10-16 08:02

About this skill

NotebookLM CLI + MCP

Programmatic access to Google NotebookLM via CLI (notebooklm) and MCP server (notebooklm-mcp). Source: https://github.com/teng-lin/notebooklm-py

When to Use

  • Research & discovery: Create notebooks, run web/Drive research agents, import sources
  • AI analysis: Query notebooks, get summaries, configure chat personas
  • Content generation: Podcasts, videos, quizzes, mind maps, briefings, flashcards, infographics, slides
  • Management: Share notebooks, source labels, sync Drive sources

CLI First (Always Primary)

Use notebooklm CLI commands via Bash tool for all operations.

# Auth
notebooklm login                                        # browser sign-in (one-time)
notebooklm auth check --test --json                     # verify auth
notebooklm doctor --fix                                 # diagnose + auto-fix issues

# Notebooks
notebooklm list
notebooklm create "Research Project" --use              # create + set active
notebooklm use <id>                                     # set active notebook
notebooklm rename "New Title"
notebooklm summary --topics

# Sources
notebooklm source add --url "https://example.com"
notebooklm source add --file /path/to/doc.pdf
notebooklm source add --text "Pasted content here"
notebooklm source add --youtube "https://youtube.com/watch?v=..."
notebooklm source list
notebooklm source delete <source-id>

# Chat
notebooklm chat ask "What are the key findings?"
notebooklm chat ask "Summarize this" --save-to-notes    # persist answer as note

# Content generation
notebooklm audio create                                 # podcast
notebooklm audio status <task-id>
notebooklm audio download <task-id> --output podcast.mp3

notebooklm video create
notebooklm video status <task-id>
notebooklm video download <task-id> --output video.mp4

notebooklm quiz create --difficulty medium
notebooklm quiz download <task-id> --output quiz.json --format json

notebooklm mind-map create
notebooklm mind-map download <task-id> --output mindmap.json

notebooklm briefing create
notebooklm flashcards create
notebooklm infographic create --orientation landscape
notebooklm slides create

# Notes
notebooklm note list
notebooklm note create "My insight"
notebooklm note delete <note-id>

# Research agents
notebooklm research add-web "enterprise AI adoption 2025"
notebooklm research add-web "cloud costs" --mode deep
notebooklm research add-drive "product roadmap"

# Sharing
notebooklm share public                                 # make public
notebooklm share invite user@example.com --role viewer

# Profiles (multiple Google accounts)
notebooklm profile create work
notebooklm profile switch work
notebooklm profile list

# MCP auto-config (run once)
notebooklm mcp install claude-code

MCP Tools (Backup Only)

Only use when CLI cannot handle the task. Common MCP tools via notebooklm-mcp:

  • notebook_list, notebook_create, notebook_delete, notebook_query
  • source_add, source_list, source_delete, source_sync_drive
  • studio_create (audio, video, quiz, mind_map, briefing, flashcards, infographic, slides)
  • download_artifact, export_artifact
  • notebook_share_public, notebook_share_invite
  • research_start (web + Drive discovery)
  • note, label, tag, batch, pipeline

Authentication

First use:

uv tool install "notebooklm-py[browser]"
notebooklm login                    # browser opens, Chromium downloads ~170MB first time
notebooklm auth check --test --json # expect {"status": "ok"}

Headless (CI/server):

notebooklm login --master-token --account you@gmail.com

Auth expires: notebooklm auth refresh or notebooklm login again.

Common Workflows

Research Notebook from Web Sources

notebooklm create "Cloud Trends 2025" --use
notebooklm research add-web "cloud marketplace growth 2025" --mode deep
notebooklm chat ask "What are the top 3 trends?"
notebooklm audio create
notebooklm audio status <task-id>          # poll until done
notebooklm audio download <task-id> --output podcast.mp3

Generate Multiple Content Types

# All generation is async — create → status → download
notebooklm audio create && notebooklm audio status <id> && notebooklm audio download <id>
notebooklm quiz create --difficulty hard && ...
notebooklm mind-map create && ...
notebooklm slides create && ...

Import from Google Drive

notebooklm research add-drive "product roadmap" --max-results 10
notebooklm source add --drive "<file-id>"

Decision Matrix: CLI vs MCP

Task Use
All single operations CLI
Batch ops across many notebooks MCP: batch tool
Pipeline (create→add→generate) MCP: pipeline tool
Cross-notebook queries MCP: cross_notebook_query
Structured output needed in-context MCP

Default: CLI for everything. MCP is backup only.

Installation

Already configured. On fresh clones, setup.sh installs via:

uv tool install "notebooklm-py[browser]"

Upgrade: uv tool upgrade notebooklm-py

Troubleshooting

notebooklm doctor --fix      # auto-detect + fix common issues
notebooklm auth check --test # test auth
notebooklm --version         # check version

Documentation