NotebookLM MCP Server
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- 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
- 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
- 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
- AlicenseNot gradedqualityDmaintenanceEnables interaction with Google NotebookLM to create notebooks, add sources (PDF, URL, YouTube), and ask questions with citations.1MIT
- AlicenseBqualityAmaintenanceEnables AI assistants to programmatically interact with Google NotebookLM, allowing them to create and manage notebooks, add sources, query content, generate audio/video, and perform research tasks through natural language commands.5319,474 PyPI6,246MIT
- AlicenseAqualityDmaintenanceEnables interaction with Google's NotebookLM via the Model Context Protocol to manage notebooks, sources, and research tasks. Users can create, query, and summarize content, as well as generate artifacts like audio overviews and slide decks using natural language.321MIT
TDQS
Scored across 32 tools
Most tools have distinct purposes targeting specific resources or actions (e.g., audio_overview_create vs. video_overview_create, notebook_add_drive vs. notebook_add_text). However, some overlap exists between research_start and notebook_query, where the descriptions clarify that research_start is for finding new sources while notebook_query is for querying existing ones, which could still cause confusion if not carefully read.
Tool names follow a highly consistent verb_noun pattern throughout (e.g., notebook_create, notebook_delete, source_list_drive, research_start). All tools use snake_case with clear, descriptive names, making the set predictable and easy to navigate.
With 32 tools, the count is borderline high for a notebook management and content generation server. While the domain is broad (notebooks, sources, research, studio artifacts), the number feels heavy and could overwhelm agents, though each tool appears to serve a specific function within the scope.
The tool surface provides comprehensive coverage for the notebook management domain, including full CRUD for notebooks and sources, content generation (audio, video, reports, etc.), research workflows, and studio operations. There are no obvious gaps; agents can perform all expected lifecycle actions from creation to deletion with detailed options.