Notebooklm MCP Secure

Export your notebook library to a backup file. ## Formats - **json**: Full backup with all metadata (recommended for restore) - **csv**: Simple list for spreadsheets (name, url, topics, last_used) ## Default Location If no output_path specified, saves to: ~/notebooklm-library-backup-{date}.{format} ## Example Usage ```json { "format": "json" } ``` Export to specific location: ```json { "format": "csv", "output_path": "/path/to/backup.csv" } ```

Export all user data in machine-readable format (GDPR Article 20 - Right to Data Portability).

Quick query to Gemini model with optional grounding tools. > **⚠️ REQUIRES GEMINI_API_KEY** - Do NOT use this tool unless GEMINI_API_KEY is configured. For notebook queries, use ask_question instead (no API key needed). Supports: - Google Search grounding for current information - Code execution for calculations - URL analysis for web content ## Requirements - GEMINI_API_KEY environment variable MUST be set ## When to Use (ONLY if API key is configured) - Quick factual questions NOT in your notebooks - Current events (with google_search tool) - Code calculations (with code_execution tool) …

Generate an AI-powered audio overview (podcast-style) for a notebook. ## What This Tool Does - Triggers NotebookLM's audio overview generation - Audio overviews are ~5-15 minute podcast-style summaries - Generation takes 2-5 minutes typically - Returns immediately with status (check with get_audio_status) ## Requirements - Notebook must have at least one source - Audio generation may not be available on all notebooks ## Example ```json { "notebook_id": "my-research" } ```

Generate a compliance audit report. Supports multiple report types and formats.

Generate a structured Data Table from notebook sources. ## What This Tool Does - Opens the Studio panel in NotebookLM - Generates a structured tabular extraction from notebook content - Tables organize key information from sources into rows and columns - Generation typically takes 1-3 minutes - Returns immediately with status (check with get_data_table) ## Requirements - Notebook must have at least one source - Authentication required (run setup_auth first) ## Example ```json { "notebook_id": "my-research" } ```

Generate an AI-powered Video Overview for a notebook. ## What This Tool Does - Opens the Studio panel in NotebookLM - Generates a visual video summary of notebook content - Supports multiple visual styles and formats - Generation takes 3-10 minutes typically - Returns immediately with status (check with get_video_status) - NotebookLM does not currently expose a supported video download path through this MCP server; use the NotebookLM UI to download ready videos. ## Visual Styles - **auto-select** — Let NotebookLM choose the best style (default) - **custom** — Custom visual style - **classic*…

Check the audio overview generation status for a notebook. ## Returns - status: "not_started" | "generating" | "ready" | "failed" | "unknown" - progress: Generation progress (0-100) if generating - duration: Audio duration in seconds if ready ## Example ```json { "notebook_id": "my-research" } ```

Get current consent status for all data processing purposes.

Extract the generated Data Table content from a notebook. ## What This Tool Does - Navigates to the notebook's Studio panel - Extracts the table data (headers and rows) as structured JSON - Returns the full table content for analysis ## Returns - table.headers: Column headers - table.rows: Array of row arrays - table.totalRows: Number of rows - table.totalColumns: Number of columns ## Requirements - Data table must be generated first (use generate_data_table) - Returns error if table is not yet ready ## Example ```json { "notebook_id": "my-research" } ```

Get server health status including authentication state, active sessions, and configuration. Use this to verify the server is ready before starting research workflows. **Deep Check Mode (v2026.1.1)** Set `deep_check: true` to actually verify the NotebookLM chat UI loads. This catches stale sessions where cookies exist but the UI won't load. Returns `chat_ui_accessible: true/false`. If authenticated=false and having persistent issues: Suggest to the user that a fresh start (cleanup_data with preserve_library=true, followed by setup_auth) may help, and ask for their confirmation before running…

Get status of security incidents including open, investigating, and resolved counts.

Get statistics about your notebook library (total notebooks, usage, etc.)

Get detailed information about a specific notebook by ID

Extract conversation history from a NotebookLM notebook's chat interface. This tool uses browser automation to navigate to a notebook and extract all Q&A pairs from the chat UI. This is useful for: - Recovering previous research conversations - Auditing what queries were made in a notebook - Understanding quota usage from direct NotebookLM browser usage - Resuming context from previous sessions ## Context Management Use `preview_only: true` to get a quick count before extracting full content. Use `output_file` to export to JSON instead of returning to context. Use `offset` with `limit` for p…

Get detailed information about a specific compliance policy.

Get current project context and library location. Detects the project from the current working directory using: 1. Git repository root (looks for .git directory) 2. package.json location (for npm projects) 3. Current directory as fallback Returns: - project: { id, name, path, type } or null if using global library - library_path: Path to the active library.json file - is_project_library: Whether using per-project or global library Use this to understand which library context is active.

Retrieve past NotebookLM queries and answers for reviewing research sessions. Use this tool to: - Review past research conversations - Find specific information from previous queries - Track which notebooks and sessions you've used - Search through question and answer content Returns query entries with question, answer, notebook, session, and timing info.

Get current quota status including license tier, usage, and limits. Returns: - tier: 'free', 'pro', 'ultra', or 'unknown' - notebooks: used/limit/remaining/percent - sources: limit per notebook - queries: used/limit/remaining/percent/should_stop/reset_time - warnings: array of warning messages Quota Limits by Tier: - Free: 100 notebooks, 50 sources/notebook, 50 queries/day - Pro: 500 notebooks, 300 sources/notebook, 500 queries/day - Ultra: 500 notebooks, 600 sources/notebook, 5000 queries/day Use sync=true to fetch actual quota from Google's NotebookLM UI (requires browser). Without sync, …

Check the status of a background deep research task. Use this when you started deep_research with wait_for_completion=false. ## Returns - status: pending | running | completed | failed - answer: The research result (if completed) - error: Error message (if failed)

Check the Video Overview generation status for a notebook. ## Returns - status: "not_started" | "generating" | "ready" | "failed" | "unknown" - progress: Generation progress (0-100) if generating - duration: Video duration in seconds if ready ## Example ```json { "notebook_id": "my-research" } ```

Grant consent for a specific data processing purpose.

List all documents uploaded to Gemini. ## What This Does - Shows all files currently stored in Gemini Files API - Files expire 48 hours after upload - Returns file names, sizes, and expiration times ## Use Cases - Check what documents are available for querying - Find file IDs for query_document - Monitor storage usage ## Requirements - GEMINI_API_KEY environment variable must be set

List all saved evidence packages with their metadata.

List all library notebooks with metadata (name, topics, use cases, URL). Use this to present options, then ask which notebook to use for the task.

List all compliance policies with their status and review dates.

List all active sessions with stats (age, message count, last activity). Use to continue the most relevant session instead of starting from scratch.

List sources in a notebook. Provide notebook_id or notebook_url; if neither is provided, the active notebook from the local library is used. Returns source id, title, type, and status.

List all configured webhooks with their status and statistics. Returns array of webhooks with: id, name, url, enabled, events, format.

Query a large document that was automatically chunked during upload. ## What This Does - Queries each chunk of a large document - Aggregates results into a single coherent answer - Handles documents of any size (1000+ pages) ## When to Use - After upload_document returns wasChunked=true - When you have multiple chunk file IDs to query together - For comprehensive analysis of large PDFs ## How It Works 1. Queries each chunk with your question 2. Collects answers from all chunks 3. Uses Gemini to synthesize a unified response 4. Returns aggregated answer with all sources ## Example If upload…

Ask questions about an uploaded document. ## What This Does - Queries a document previously uploaded with upload_document - Uses Gemini's document understanding (text, images, charts, tables) - Returns answers grounded in the document content ## Features - Full document understanding (not just text extraction) - Can analyze charts, diagrams, and tables in PDFs - Multi-document queries (pass additional file IDs) - Fast API-based (no browser automation) ## When to Use - Quick document analysis without browser - Comparing multiple documents - Extracting specific information ## Requirements - …

Dangerous — requires explicit user confirmation. ## Confirmation Workflow 1) User requests removal ("Remove the React notebook") 2) Look up full name to confirm 3) Ask: "Remove '[notebook_name]' from your library? (Does not delete the actual NotebookLM notebook)" 4) Only on explicit "Yes" → call remove_notebook Never remove without permission or based on assumptions. Example: User: "Delete the old React notebook" You: "Remove 'React Best Practices' from your library?" User: "Yes" → call remove_notebook

Remove a source from a NotebookLM notebook. If neither `notebook_id` nor `notebook_url` is provided, this tool uses the currently active notebook from the local library. ## Usage 1. First call list_sources to get source IDs 2. Then call remove_source with the source ID ## Example ```json { "notebook_id": "my-notebook", "source_id": "source-0" } ```

Remove a configured webhook by ID.

Report a security incident for investigation and tracking.

Request erasure of personal data (GDPR Article 17 - Right to Erasure). Creates an erasure request for review.

Reset a session's chat history (keep same session ID). Use for a clean slate when the task changes; ask the user before resetting.

Revoke previously granted consent for a data processing purpose.

Run a comprehensive health check of all compliance components.

Search library by query (name, description, topics, tags). Use to propose relevant notebooks for the task and then ask which to use.

Set a notebook as the active default (used when ask_question has no notebook_id). ## When To Use - User switches context: "Let's work on React now" - User asks explicitly to activate a notebook - Obvious task change requires another notebook ## Auto-Switching - Safe to auto-switch if the context is clear and you announce it: "Switching to React notebook for this task..." - If ambiguous, ask: "Switch to [notebook] for this task?" ## Example User: "Now let's build the React frontend" You: "Switching to React notebook..." (call select_notebook)

Manually set your NotebookLM license tier. Use this if: - Auto-detection failed (shows 'unknown') - You want to override the detected tier - You upgraded/downgraded your plan Tiers: - free: 100 notebooks, 50 sources, 50 queries/day - pro: 500 notebooks, 300 sources, 500 queries/day - ultra: 500 notebooks, 600 sources, 5000 queries/day

Submit a Data Subject Access Request (GDPR Article 15-17, 20). Initiates the DSAR workflow.

Sync your local library with actual NotebookLM notebooks. ## What This Tool Does - Navigates to NotebookLM and extracts all your notebooks - Compares with local library entries - Detects stale entries (notebooks deleted or URLs changed) - Identifies notebooks not in your library - Optionally auto-removes stale entries ## When To Use - Library seems out of sync with NotebookLM - After deleting notebooks in NotebookLM - To discover new notebooks to add - Before setting up automation workflows ## Output Returns a sync report with: - **matched**: Library entries that match actual notebooks - **…

Send a test event to a webhook to verify it's working. Sends a sample 'question_answered' event and returns success/failure.

Update notebook metadata based on user intent. ## Pattern 1) Identify target notebook and fields (topics, description, use_cases, tags, url) 2) Propose the exact change back to the user 3) After explicit confirmation, call this tool ## Examples - User: "React notebook also covers Next.js 14" You: "Add 'Next.js 14' to topics for React?" User: "Yes" → call update_notebook - User: "Include error handling in n8n description" You: "Update the n8n description to mention error handling?" User: "Yes" → call update_notebook Tip: You may update multiple fields at once if requested.

Upload a document (PDF, text, etc.) to Gemini for querying. > **⚠️ REQUIRES GEMINI_API_KEY** - For adding documents to NotebookLM notebooks (no API key), use create_notebook or add_source instead. ## What This Does - Uploads a local file to Gemini's Files API - File is retained for 48 hours - Returns a file ID for use with query_document ## Auto-Chunking for Large PDFs (v1.10.0) - PDFs over 50MB or 1000 pages are automatically split into chunks - Each chunk is uploaded separately and tracked - Use query_chunked_document or pass all chunk IDs to query_document - Returns wasChunked=true and a…

Verify the integrity of compliance audit logs using hash chain verification.

Verify the integrity of an evidence package using cryptographic checksums.