Plugin
paper-trail
Anti-hallucination plugin for academic research in Claude Code. Create literature reviews and papers guaranteed without fabricated citations.
6 skills6 agents3 hooks22 commands+3+2+2+2+1+1+1+16
@roomi-fieldsResearch
Plugin
notebooklm
Automate Google NotebookLM at scale. 53-endpoint HTTP REST API for n8n / Zapier / Make / curl, plus an MCP server for Claude Code / Cursor / Codex.
Plugin Signal66/100Solid
maintained98
capable14
documented100
adopted50
Install
$ npx -y skills add roomi-fields/notebooklm-mcp --agent claude-code
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Maintenance
TypeScript
Language
MIT
License
4d ago
Last commit
11mo ago
Created
8d ago
Added
What's inside
README
NotebookLM REST API + MCP server
Automate Google NotebookLM at scale. 53-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.
v4.4.0 — 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 internalbatchexecuteRPC 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
notebooklmname.
Unofficial project — good to know before you start
This is not affiliated with Google. It talks to the same
batchexecuteendpoints 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.
What You Can Build
🔗 No-code automation pipelines — The 53-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_batchwrites 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.
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_batchexists because a thesis needed it. -
🔌 Replacing a RAG engine with the REST API —
musnymubarak/Calim_Docswapped 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.
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 — orpaginate: falsefor the whole document at once.
Notebook Summary and Chat History
- What is in this notebook? (
get_notebook_summary) — NotebookLM's own summary of its sources plus the questions it suggests asking, without spending a question to find out
File tree
More plugins by @roomi-fields.
View @roomi-fields’s profile@roomi-fieldsFAQ
notebooklm is a Claude Code plugin with 1 hand-picked skill, indexed on Flowy in the Research category. Install it with the command on its page. It includes notebooklm. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
- What is notebooklm?
- Automate Google NotebookLM at scale. 53-endpoint HTTP REST API for n8n / Zapier / Make / curl, plus an MCP server for Claude Code / Cursor / Codex.
- How do I install notebooklm?
- Run these in Claude Code: npx -y skills add roomi-fields/notebooklm-mcp --agent claude-code. Then prompt normally.
- Does notebooklm auto-invoke its skills?
- Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.
- Is notebooklm free?
- Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.
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