NotebookLM MCP Server
by Ronald564
README.md
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# NotebookLM REST API + MCP server
**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](./CHANGELOG.md). [Compare with `PleasePrompto/notebooklm-mcp`](https://roomi-fields.github.io/notebooklm-mcp/compare) 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](#disclaimer) for the full text.
---
## 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](https://schemas.roomi-fields.com/nblm-answer-v1.json), 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.
---
## 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`](#features) exists because a thesis
needed it.
- **🔌 Replacing a RAG engine with the REST API** — [`musnymubarak/Calim_Doc`](https://github.com/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:
| Content Type | Formats | Options |
| ------------------ | ------------------------ | ---------------------------------------------- |
| **Audio Overview** | Podcast-style discussion | Language (80+), custom instructions |
| **Video** | Brief, Explainer | 6 visual styles, language, custom instructions |
| **Infographic** | Horizontal, Vertical | Language, custom instructions |
| **Report** | Summary, Detailed | Language, custom instructions |
| **Presentation** | Overview, Detailed | Language, custom instructions |
| **Data Table** | Simple, Detailed | Language, custom instructions |
| **Flashcards** | Study cards | Language, custom instructions |
| **Quiz** | Assessment questions | Language, custom instructions |
| **Mind Map** | Interactive node graph | Saved to the notebook |
**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](./CHANGELOG.md).
### 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`)
- **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 Library
- **Multi-notebook management** with validation and smart selection
- **Auto-discovery**: Automatically generate metadata via NotebookLM queries
- **Search notebooks** by keyword in name, description, or topics
- **Scrape notebooks**: List all notebooks from NotebookLM with IDs and names
- **Bulk delete**: Delete multiple notebooks at once
### Accounts & Localization
- **Personal _and_ Google Workspace accounts** — recognizes both NotebookLM hosts (`notebooklm.google.com` and the `notebook.google.com` Workspace alias), so Workspace sessions authenticate cleanly instead of looping on "session expired"
- **UI-language-aware** — drives NotebookLM whether its interface is in English, French, German, or Japanese (`en` · `fr` · `de` · `ja`); [add a language](./docs/ADDING_A_LANGUAGE.md) in a single JSON file
### Integration Options
- **MCP Protocol** — Claude Code, Cursor, Codex, any MCP client
- **Agent Skill** — ships a bundled [`notebooklm` skill](./skills/notebooklm/SKILL.md) (also standalone: [`roomi-fields/notebooklm-skill`](https://github.com/roomi-fields/notebooklm-skill)) that teaches the agent citation formats, the daily-quota-aware batch pattern, and when to use which transport
- **HTTP REST API** — n8n, Zapier, Make.com, custom integrations
- **Docker** — Isolated deployment with Docker or Docker Compose
- **[RTFM](https://github.com/roomi-fields/rtfm) retrieval layer** — `/batch-to-vault` writes citation-backed answers as markdown + JSON sidecars (`nblm-answer-v1` schema), indexable by [RTFM](https://github.com/roomi-fields/rtfm) (FTS5 + semantic) for unlimited offline queries. Ideal for academic / SOTA workflows. [Guide](./deployment/docs/14-RTFM-INTEGRATION.md).
---
## Quick Start
### Option 0 — Claude Code marketplace (one-liner, recommended for Claude Code users)
The fastest way to get NotebookLM into Claude Code. Distributed via the [`roomi-fields/claude-plugins`](https://github.com/roomi-fields/claude-plugins) marketplace alongside [RTFM](https://github.com/roomi-fields/rtfm) (the retrieval companion — see [RTFM integration guide](./deployment/docs/14-RTFM-INTEGRATION.md)):
```text
/plugin marketplace add roomi-fields/claude-plugins
/plugin install notebooklm@roomi-fields
```
That registers the MCP server, runs `npx -y @roomi-fields/notebooklm-mcp@<pinned-version>` automatically (Node ≥ 18 required), and lets you upgrade with two commands when a new release ships: `/plugin marketplace update roomi-fields` then `/reload-plugins`. Then run `npx -y -p @roomi-fields/notebooklm-mcp notebooklm-mcp-setup-auth` once in a terminal to log into Google (a visible Chrome opens). To install RTFM at the same time: `/plugin install rtfm@roomi-fields`.
### Option 1 — HTTP REST API (n8n, Zapier, Make, curl, any HTTP client)
```bash
git clone https://github.com/roomi-fields/notebooklm-mcp.git
cd notebooklm-mcp
npm install && npm run build
npm run setup-auth # One-time Google login
npm run start:http # Start REST API on port 3000
```
```bash
# Citation-backed Q&A, single curl, JSON response
curl -X POST http://localhost:3000/ask \
-H 'Content-Type: application/json' \
-d '{"question": "Summarize chapter 3", "notebook_id": "your-id", "source_format": "json"}'
```
The full surface is **33 documented endpoints** — see the [REST API reference](https://roomi-fields.github.io/notebooklm-mcp/notebooklm-rest-api). For overnight batches of 1 000+ questions, see the [batch pattern](https://roomi-fields.github.io/notebooklm-mcp/batch-1000-questions).
### Option 2 — MCP Mode (Claude Code, Cursor, Codex)
```bash
# Build (same package, MCP transport)
git clone https://github.com/roomi-fields/notebooklm-mcp.git
cd notebooklm-mcp
npm install && npm run build
# Claude Code
claude mcp add notebooklm node /path/to/notebooklm-mcp/dist/index.js
# Cursor — add to ~/.cursor/mcp.json
{
"mcpServers": {
"notebooklm": {
"command": "node",
"args": ["/path/to/notebooklm-mcp/dist/index.js"]
}
}
}
```
**Log in once — in a terminal, not through the assistant.** Run the interactive
Google login as a command; a visible Chrome window opens, you sign in, and the
saved session is then reused by the MCP server:
```bash
npm run setup-auth # from a clone (Option 2 above)
notebooklm-mcp setup-auth # from a global install (npm i -g @roomi-fields/notebooklm-mcp)
```
Do the login in a terminal rather than by asking the assistant _"log me in"_:
some stdio MCP clients (e.g. Claude Desktop) cap tool-call duration and cut off
the up-to-10-minute interactive login before you can finish signing in (see
[issue #27](https://github.com/roomi-fields/notebooklm-mcp/issues/27)).
### Option 3 — Docker (NAS, server, headless)
```bash
# Build and run
docker build -t notebooklm-mcp .
docker run -d --name notebooklm-mcp -p 3000:3000 -p 6080:6080 -v notebooklm-data:/data notebooklm-mcp
# Authenticate via noVNC
# 1. Open http://localhost:6080/vnc.html
# 2. Run: curl -X POST http://localhost:3000/setup-auth -d '{"show_browser":true}'
# 3. Login to Google in the VNC window
```
See [Docker Guide](./deployment/docs/08-DOCKER.md) for NAS deployment (Synology, QNAP).
---
## Documentation
Full docs site: **<https://roomi-fields.github.io/notebooklm-mcp/>** · [OpenAPI 3.1 spec](./deployment/docs/openapi.yaml)
| Guide | Description |
| -------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| [Installation](./deployment/docs/01-INSTALL.md) | Step-by-step setup for HTTP and MCP modes |
| [Configuration](./deployment/docs/02-CONFIGURATION.md) | Environment variables and security |
| [REST API reference](./deployment/docs/03-API.md) | Complete HTTP endpoint documentation (33 endpoints) |
| [Run 1 000 questions overnight](./deployment/docs/12-BATCH-1000.md) | Production batch pattern with auto-reauth and rotation |
| [**RTFM integration — cache as searchable vault**](./deployment/docs/14-RTFM-INTEGRATION.md) | Pipeline pattern: NotebookLM as one-shot ingestion, RTFM as retrieval layer. `/batch-to-vault` endpoint, `nblm-answer-v1` schema. |
| [n8n integration](./deployment/docs/04-N8N-INTEGRATION.md) | Workflow automation setup |
| [Troubleshooting](./deployment/docs/05-TROUBLESHOOTING.md) | Common issues and solutions |
| [Notebook library](./deployment/docs/06-NOTEBOOK-LIBRARY.md) | Multi-notebook management |
| [Auto-discovery](./deployment/docs/07-AUTO-DISCOVERY.md) | Autonomous metadata generation |
| [Content management](./deployment/docs/10-CONTENT-MANAGEMENT.md) | Audio, video, infographic, report, presentation |
| [Multi-account rotation](./deployment/docs/11-MULTI-ACCOUNT.md) | Multiple accounts with TOTP auto-reauth |
| [Docker](./deployment/docs/08-DOCKER.md) | Docker and Docker Compose deployment |
| [Multi-interface](./deployment/docs/09-MULTI-INTERFACE.md) | Run Claude Desktop + HTTP simultaneously |
| [**Compare with PleasePrompto v2.0.0**](./deployment/docs/13-COMPARE.md) This server cannot be deployed
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