roomi-fields/notebooklm-mcp

**Automate Google NotebookLM at scale. 33-endpoint HTTP REST API for n8n / Zapier / Make / curl, plus an MCP server for Claude Code / Cursor / Codex. 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.**
> v3.2.0 — **generated content can finally be deleted** (content_delete): the endpoint had been declared and called by nothing since v3, so notebooks accumulated every draft ever asked for. Builds on 3.1.x, where **generated content stopped coming back in the wrong language** — the interface locale was overriding the language argument on both transports, silently, while reporting success. Also: reading a source's full indexed text (source_read, paginated), working source labels, and RPC refusals reported as refusals instead of as a rotated endpoint id. 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, both shipped permanently. Batch-tested on overnight runs of 1 000+ questions. See the changelog. Compare with PleasePrompto/notebooklm-mcp for when this project is the right pick (REST API, full Studio, auto-reauth).
> **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 33-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.