Notebooklm mcp server
About
Let your AI agents chat directly with Google NotebookLM for zero-hallucination answers.
Details
- Author
- moodrobotics
- Categories
- Productivity, Other, AI
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Setup
Install Notebooklm mcp server in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/moodrobotics/notebooklm-mcp-server
Follow the installation instructions in the repository README, then restart your MCP client.
Import web pages and YouTube videos to NotebookLM with one click. Trusted by 200,000+ users.
Give your AI agents full control of Google Gemini Notebook (formerly NotebookLM): grounded answers, deep research, podcasts, quizzes, and more — 47 tools, zero hallucinations.
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Installation•Authentication•Connect Your AI Client•Tool Reference•Recipes•Development
Google'sGemini Notebook(renamed from NotebookLM in July 2026) is the best grounded-answer engine on the market: it only answers from the sources you give it, with citations. This MCP server hands that power to your AI agents — Claude, Gemini, Antigravity, Cursor and any other MCP client — so they can build notebooks, run Deep Research, generate podcasts, videos, quizzes, flashcards, reports and mind maps, take notes, and share the results with your team.All programmatically, with zero hallucinations.
[!NOTE]NotebookLM is now Gemini Notebook.Google renamed the product on July 16, 2026 (seenotebook.google). Same product, same notebooks, same API surface — this server keeps working unchanged, and the npm package keeps itsnotebooklm-mcp-servername.
[!NOTE] The server checks for updates on startup and keeps itself current automatically.
npx -y notebooklm-mcp-server auth # authenticate npx -y notebooklm-mcp-server start # run the server
- Run the interactive login (opens a Chromium window):
Done — the server loads the session automatically and refreshes the rotating token by itself.
claude mcp add notebooklm -- npx -y notebooklm-mcp-server start
Add toclaude_desktop_config.json(Settings → Developer → Edit Config):
{ "mcpServers": { "notebooklm": { "command": "npx", "args": ["-y", "notebooklm-mcp-server", "start"] } } }
Antigravity's CLI manages MCP servers through a JSON config file:
- Global:~/.gemini/config/mcp_config.json
- Per-workspace:.agents/mcp_config.jsonin your project
{ "mcpServers": { "notebooklm": { "command": "npx", "args": ["-y", "notebooklm-mcp-server", "start"] } } }
Then type/mcpin the Antigravity prompt panel to open theMCP Manager: check the connection status ring, reload the config, or inspect logs — no restart needed. Your agents can now cite real sources in every mission.
The IDE reads the same config files as the CLI (~/.gemini/config/mcp_config.jsonor.agents/mcp_config.json). Alternatively, open theMCP Storepanel, chooseAdd custom server, and paste the same JSON snippet. Reload the server list and the NotebookLM tools appear in the agent's toolbox.
gemini mcp add notebooklm --scope user -- npx -y notebooklm-mcp-server start
Add to.cursor/mcp.jsonin your project (or~/.cursor/mcp.jsonglobally):
{ "mcpServers": { "notebooklm": { "command": "npx", "args": ["-y", "notebooklm-mcp-server", "start"] } } }
Add the samemcpServersblock to~/.codeium/windsurf/mcp_config.json.
In Cline:MCP Servers → Configureand add the samemcpServersblock shown above.
47 tools, grouped by what they do. Full parameter signatures live indocs/TOOLS.md.
Real prompts you can paste into any connected agent — it picks the right tools by itself.
"Research the latest on solid-state batteries with deep research, build a notebook called 'Solid State 2026' with the best sources, and generate a brief audio overview in English. Give me the link when it's done."
The agent chainsnotebook_create→research_start(mode: deep)→research_poll→research_import→audio_overview_create(format: brief)→studio_poll, and returns the notebook URL with the finished podcast.
"Take the three PDFs in ./lectures, put them in a new notebook, and make me a hard quiz plus flashcards on the key concepts. Show me the quiz questions here."
The agent runsnotebook_add_local_fileper PDF, thenquiz_create(difficulty: hard)+flashcards_create, polls withstudio_poll, and pulls the questions out withartifact_content_get.
"Build a briefing notebook from these five links about our competitor, write a report, export it to Google Docs, and share the notebook withana@example.comas a viewer."
The agent chainsnotebook_add_url×5 →report_create→studio_poll→artifact_export(format: docs)→notebook_share(user_email, user_role: viewer)— and your teammate finds the notebook and the Doc waiting.
git clone https://github.com/moodRobotics/notebooklm-mcp-server.git cd notebooklm-mcp-server npm install npm run build npm run typecheck
Architecture notes live indocs/ARCHITECTURE.md, the roadmap indocs/ROADMAP.md.
This README is available inEnglish,Español,Français,PortuguêsandDeutsch. If you edit one, please keep the others in sync (npm run docs:check).
MIT — Developed with ❤️ bymoodRobotics.
[!IMPORTANT] This is an unofficial community project. It is not affiliated with or endorsed by Google. It relies on reverse-engineered internal APIs that may change without notice; use it with a Google account you are comfortable automating.
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