notebooklm-mcp: MCP server for NotebookLM - Let your AI agents research documentation directly with grounded, citation-backed

Last updated: 2026-09-10 · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install notebooklm-mcp

npx notebooklm-mcp@latest
git clone https://github.com/PleasePrompto/notebooklm-mcp

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 75/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars	3,414
Forks	508
Language	TypeScript
Category	MCP Server
License	MIT
Quality Score	75/100
Last Updated	2026-09-10
Created	2025-10-16
Platforms	claude-code, cli, codex, gemini, mcp, node
Est. Tokens	~8k

About notebooklm-mcp

NotebookLM MCP Server MCP server for Google NotebookLM. It drives a real Chrome via Patchright (stealth + persistent fingerprint) so an agent can chat against a notebook, ingest sources, generate audio overviews, and read DOM-level citations. Two transports are supported: (default) and Streamable-HTTP. v2.0.0 is the current line; v1 is no longer supported.

Compatible Skills

These tools work well together with notebooklm-mcp for enhanced workflows:

davia — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
claude-code-hub — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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