notebooklm-mcp: Google Gemini Notebook (formerly NotebookLM) over MCP + a local HTTP REST API

by roomi-fields · MCP Server · ★ 229

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

How to install notebooklm-mcp

git clone https://github.com/roomi-fields/notebooklm-mcp.git
docker run -d --name notebooklm-mcp -p 3000:3000 -p 6080:6080 -v notebooklm-data:/data notebooklm-mcp

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 73/100

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.

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.

agentic-skillanthropicautomationcitationsclaude-codeclaude-skillscodexgemini-notebookgemini-notebook-apigoogle-notebooklm

Quick Facts

Stars229
Forks66
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score73/100
Last Updated2026-11-07
Created2025-11-23
Platformsclaude-code, codex, gemini, mcp, node
Est. Tokens~18k

About notebooklm-mcp

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.

Compatible Skills

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

  • notebooklm-ai-plugin — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • notebooklm-mcp-secure — semantic(0.38)+rare_topics+same_lang+similar_pop+shared_platform (48%)
  • notebooklm-py — semantic(0.32)+complementary+rare_topics+shared_platform (46%)
  • podcast-llm — semantic(0.16)+complementary+rare_topics+similar_pop+shared_platform (45%)

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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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