NotebookLM MCP

NotebookLM REST API + MCP server

Automate Google Gemini Notebook — formerly, and still widely known as, NotebookLM — at scale. 56-endpoint HTTP REST API for n8n / Zapier / Make / curl, an MCP server for Claude Code / Cursor / Codex, and a UTCP interface for clients that call the API natively. Citation-backed Q&A, full Studio generation (audio · video · infographic · report · presentation · data table), multi-account rotation with auto-reauth across personal and Google Workspace accounts.

v4.5.0 — Node 22 is now the floor, and the last four open advisories are gone with it. The declared floor of Node 18 had quietly become fiction: three production dependencies already required Node 20 or newer, and the test matrix kept passing on 18 without exercising them. Raising it also let the third-party file globber go — its dependency chain carried four advisories with no fix upstream — replaced by Node's built-in search, now actually under test. Builds on 4.0.0, where the HTTP API stopped being open to the network and to any web page (GHSA-p69m-c5rf-g2c3): it binds loopback, refuses unlisted origins, and takes a bearer token — remote access and Docker require `NOTEBOOKLM_API_KEY`, see Securing the HTTP API. Built on a dual transport — the internal batchexecute RPC API (10-100× faster than scraping, immune to UI rebrands) with the Playwright browser as an automatic fallback. See the changelog.

Note (July 2026): Google rebranded NotebookLM to Gemini Notebook. It is the same product, existing links redirect, and this project drives the same underlying service — the browser path was updated for the new DOM in v2.3.0 and the RPC path in v3.0.0. Package and repository keep the notebooklm name.

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Unofficial project — good to know before you start

This is not affiliated with Google. It talks to the same batchexecute

endpoints the NotebookLM web app uses, with a browser fallback when they move.

They are undocumented, so they can change without notice — when that happens we

ship a fix, as we have for every change so far.

Two practical notes: use a dedicated Google account for automation, and

expect NotebookLM's own quotas to apply at high volume. See

Disclaimer for the full text.

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What You Can Build

🔗 No-code automation pipelines — The 56-endpoint REST API means NotebookLM becomes a step in n8n, Zapier, Make, or a plain curl in cron. No agent, no MCP client, no Node in your stack — just HTTP. This is the half most NotebookLM libraries don't have.

🤖 Agent tooling — The same engine over MCP for Claude Code, Cursor and Codex, with a bundled skill that primes the agent on citation formats, the daily-quota-aware batch pattern, and transport selection.

📚 Research at volume — Multi-account rotation with automatic re-authentication, built for overnight runs of 1 000+ questions across several notebooks without babysitting.

🎙️ Full Studio generation — Audio overviews, video, infographics, reports, presentations, data tables, plus flashcards, quizzes and mind maps — generated and downloaded programmatically.

Use Cases & Recipes

NotebookLM is a grounded engine: Gemini reads your sources and answers _from them_, with citations. The winning pattern is to let it do the expensive reading while your own stack handles orchestration and the last mile.

Spend fewer tokens — offload the reading

• 🪙 Zero-token synthesis layer — Drop 30 documents in a notebook, let Gemini do the heavy analysis, and spend your agent's context only on the final polish. The reasoning happens server-side; your agent just orchestrates (add_notebook → source_add → notebook_ask).
• 💾 Answer cache you can re-read offline — vault_batch writes every answer to disk as structured JSON against a published schema, so a batch run becomes a corpus you can grep, diff, re-index, or feed to a retrieval layer — without re-querying and re-spending quota.

Wire it into things that aren't agents

• ⚙️ NotebookLM as an n8n / Zapier / Make step — Because it speaks plain HTTP, a citation-backed answer becomes one node in a workflow: a form submission triggers a question, the cited answer lands in a sheet, a Slack message, or a database. No agent runtime involved.
• 📄 Document intake pipeline — Watch a folder or an inbox, push new PDFs and URLs in as sources, and ask a standing set of questions against them on every arrival.

Grounded answers with a paper trail

• 🔍 Citations with the actual source text — Answers come back with source names _and the quoted excerpts_ they rest on, extracted from the citation panel — so a claim can be checked, not just attributed.
• 🎓 Literature review at thesis scale — Batch 100+ research questions across multiple notebooks, rotate accounts as daily quotas run out, and resume where it stopped. Built for, and tested on, exactly this.

Get artifacts back out

• 🔁 One source set, every format — Fan a single notebook out to a podcast, a video, a slide deck, a report, a quiz and a mind map, then download them all locally.

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In the Wild

Real deployments, not hypotheticals.

• 📚 A doctoral literature review at batch scale — The project was built for, and is continuously tested on, overnight runs of 1 000+ research questions spread across several notebooks: multi-account rotation picks up when a daily quota runs out, every answer is written to disk with its citations, and an interrupted run resumes instead of starting over. The batch pattern in vault_batch exists because a thesis needed it.

• 🔌 Replacing a RAG engine with the REST API — musnymubarak/Calim_Doc swapped a Gemini-based retrieval engine for this project's HTTP API, running it as a Docker service (notebooklm:3000) behind a full client and worker layer. A good illustration of the REST half: no agent runtime, no MCP client — NotebookLM simply became a backend service their Python app calls.

Built something with it? Open an issue — this section is for other people's work.

Features

Q&A with Citations

• Ask questions to NotebookLM and get accurate, citation-backed answers
• Source citation extraction with 5 formats: none, inline, footnotes, json, expanded (97% excerpt success rate)
• Session management for multi-turn conversations with auto-reauth on session expiry

Content Generation

Generate multiple content types from your notebook sources:

Video Visual Styles: classroom, documentary, animated, corporate, cinematic, minimalist

Language of generated content: pass language to any generator — a BCP-47 code (es, ja, pt_BR, zh_Hans) or a name in English or in the language itself ("Spanish", "Español"). 81 languages are accepted, and an unrecognised one is refused rather than quietly swapped for another. Set a default with NOTEBOOKLM_CONTENT_LANGUAGE; it is deliberately independent of NOTEBOOKLM_UI_LOCALE, which only picks the interface language the browser fallback reads.

Flashcards and quizzes are generated via generate_study_aid; mind maps via generate_mind_map. v3 also adds share_notebook, manage_labels, and research_sources (web/Drive source discovery) — see the changelog.

Content Download

• Download Audio — WAV audio files
• Download Video — MP4 video files
• Download Infographic — PNG image files
• Text-based content (report, presentation, data_table) is returned in the API response
• Delete generated content (content_delete) — until now a notebook accumulated every draft anyone ever asked for, with no way to remove one short of the web UI

Source Management

• Add sources: Files (PDF, TXT, DOCX), URLs, Text, YouTube videos, Google Drive
• List sources: Every source with its ID and title (source_list)
• Rename a source (rename_source): fix a title in place, by id or by name
• Check freshness (check_source_freshness): which URL / Drive sources have drifted from what they point at — the whole notebook in one call. An unanswered source is reported as unknown, never as current
• Read a source's guide (get_source_guide): NotebookLM's own summary and key topics for one source
• Read a source in full (source_read): the exact text NotebookLM indexed — what it actually reasons over, which the web UI only shows in fragments. Quote a source verbatim, check what a PDF really yielded, or hand the raw material to another tool. Name the source instead of its ID if you prefer; an ambiguous name is refused rather than guessed. Long sources arrive one page at a time, with an explicit instruction for fetching the next — or paginate: false for the whole document at once.

Notebook Summary and Chat History

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