notebooklm-mcp
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- AlicenseAqualityDmaintenanceEnables AI assistants to programmatically access and control Google NotebookLM, supporting operations like notebook management, source addition, audio generation, and more via natural language.39MIT
- AlicenseBqualityBmaintenanceEnables AI assistants to programmatically access and manage Gemini Notebook (formerly Google NotebookLM), including creating notebooks, adding sources, querying content, generating studio content, downloading artifacts, and running batch or cross-notebook workflows through natural language.49MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to manage Google NotebookLM notebooks, add sources from URLs, YouTube, Drive, or text, query and summarize content, and generate audio overviews, infographics, and slide decks through natural language.MIT
- AlicenseAqualityNot gradedmaintenanceEnables interaction with Google's NotebookLM through natural language, allowing users to create and manage notebooks, add sources from URLs/YouTube/Google Drive, query AI for insights, generate audio podcasts and other studio content, and perform AI-powered research and analysis.323MIT
- AlicenseAqualityCmaintenanceEnables AI agents to interact with Google NotebookLM, including listing and managing notebooks and sources, asking grounded questions with citations, and generating audio overviews, briefings, quizzes, and mind maps.13MIT
- AlicenseAqualityDmaintenanceEnables interaction with Google NotebookLM through natural language to create and manage notebooks, add sources from URLs/YouTube/Google Drive, perform AI-powered research and analysis, generate audio podcasts, videos, infographics, and slide decks from notebook content.32MIT
TDQS
Scored across 50 tools
Most tools separate cleanly by resource (notebook_, source_, collection_, studio_, research_), but several overlap: cross_notebook_query vs. batch's query action, report vs. studio_create, and download_artifact/export_artifact/download_all_artifacts could be confused. Descriptions mostly clarify intent, so an agent can usually pick correctly, but the boundaries are not always crisp.
The resource-prefix pattern (notebook_get, collection_list, source_add) is visible but broken by bare-noun umbrella tools like tag, label, note, batch, pipeline, and report. Mixing noun_verb names with action-first names like download_artifact and research_start makes the naming convention unpredictable.
50 tools is far beyond the well-scoped range, even though several tools consolidate many sub-operations. The broad NotebookLM domain justifies some sprawl, but the overall surface is heavy and burdensome for an agent to navigate.
The tool set covers notebook/source/collection CRUD, sharing, research workflows, chat, studio artifact creation/download/export/delete, auth, usage, labels, notes, tags, and batch operations. Minor gaps such as collaborator removal or a general artifact listing don't create fatal dead ends.