notebooklm-mcp-programmatic-access

# NotebookLM MCP & CLI > Skill by [ara.so](https://ara.so) — Devtools Skills collection. **notebooklm-mcp-cli** provides programmatic access to Google NotebookLM through both a command-line interface (`nlm`) and Model Context Protocol (MCP) server (`notebooklm-mcp`). Use it to automate research workflows, generate AI podcasts, manage notebooks, and integrate NotebookLM into AI coding agents. ## What It Does - **Notebook Management**: Create, list, delete, and share NotebookLM notebooks - **Source Management**: Add sources from URLs, text, Google Drive, or local files - **AI Studio**: Generate audio podcasts, video presentations, slide decks, and infographics - **Query & Research**: Query notebooks, perform web/Drive research, cross-notebook queries - **Automation**: Batch operations, pipelines, tagging, and smart selection - **MCP Integration**: Connect Claude, Gemini, Cursor, and other AI tools to NotebookLM - **Profile Support**: Manage multiple Google accounts with isolated browser sessions ## Installation ### Using uv (Recommended) ```bash uv tool install notebooklm-mcp-cli ``` ### Using pip ```bash pip install notebooklm-mcp-cli ``` ### Using pipx ```bash pipx install notebooklm-mcp-cli ``` After installation, you get: - `nlm` — CLI command - `notebooklm-mcp` — MCP server executable ## Authentication Before using, authenticate with your Google account: ```bash # Auto mode: launches browser, extracts cookies automatically nlm login # Check authentication status nlm login --check # Use named profiles for multiple Google accounts nlm login --profile work nlm login --profile personal # Switch default profile nlm login switch personal # List all profiles nlm login profile list # Manual mode: import cookies from file nlm login --manual --file cookies.txt ``` **Profile Management:** ```bash nlm login profile list # Show all profiles with emails nlm login profile delete work # Delete a profile nlm login profile rename old new # Rename a profile ``` Each profile maintains its own browser session and cookies, allowing simultaneous use of multiple Google accounts. ## CLI Quick Start ### Basic Workflow ```bash # List existing notebooks nlm notebook list # Create a new notebook nlm notebook create "AI Research Project" # Add sources (URL, text, Drive, or file) nlm source add <notebook-id> --url "https://example.com/article" nlm source add <notebook-id> --text "Raw text content here" nlm source add <notebook-id> --drive "https://docs.google.com/document/d/..." nlm source add <notebook-id> --file ./document.pdf # Query the notebook nlm notebook query <notebook-id> "What are the key findings?" # Generate a podcast nlm studio create <notebook-id> --type audio --confirm # Download the audio file nlm download audio <notebook-id> <artifact-id> # Share notebook publicly nlm share public <notebook-id> ``` ### Studio Content Types ```bash # Generate audio podcast nlm studio create <notebook-id> --type audio --confirm # Create video presentation nlm studio create <notebook-id> --type video --confirm # Generate slide deck nlm studio create <notebook-id> --type slides --confirm # Create infographic nlm studio create <notebook-id> --type infographic --confirm # Revise existing slide deck nlm slides revise <notebook-id> <artifact-id> "Make it more technical" ``` ### Advanced Features ```bash # Batch query multiple notebooks nlm batch query "What are the main themes?" --tag research # Cross-notebook query nlm cross query "Compare findings across all notebooks" --tag project-alpha # Run a pipeline nlm pipeline run research-workflow --input '{"topic": "quantum computing"}' # Tag notebooks for organization nlm tag add <notebook-id> research ai ml nlm tag list nlm tag select --tag research --limit 5 # Web and Drive research nlm research start <notebook-id> --query "latest AI developments" --max-results 10 ``` ### Configuration ```bash # Set preferred browser for authentication nlm config set auth.browser brave # Set default profile nlm config set auth.default_profile work # View all settings nlm config list ``` ## MCP Server Setup ### Automatic Configuration ```bash # Add to Claude Code nlm setup add claude-code # Add to Claude Desktop nlm setup add claude-desktop # Add to Gemini CLI nlm setup add gemini # Add to Cursor nlm setup add cursor # Add to GitHub Copilot nlm setup add github-copilot # Add to Windsurf nlm setup add windsurf # Generate JSON for other tools nlm setup add json # List configured tools nlm setup list # Remove from a tool nlm setup remove claude-code ``` ### Manual Configuration If automatic setup doesn't work, add to your MCP client's config: **Claude Desktop/Code** (`~/Library/Application Support/Claude/claude_desktop_config.json`): ```json { "mcpServers": { "notebooklm": { "command": "notebooklm-mcp" } } } ``` **Cursor** (`.cursor/mcp_config.json`): ```json { "mcpServers": { "notebooklm": { "command": "notebooklm-mcp" } } } ``` ## MCP Tools Reference The MCP server provides 35 tools. Key tools include: ### Notebook Operations - `notebook_list` — List all notebooks - `notebook_create` — Create new notebook - `notebook_delete` — Delete notebook - `notebook_query` — Query notebook (persists to web UI) - `notebook_get` — Get notebook details - `notebook_share_public` — Enable public sharing - `notebook_share_invite` — Share with specific users ### Source Management - `source_add` — Add URL, text, Drive, or file source - `source_list` — List sources in notebook - `source_delete` — Remove source - `source_sync_drive` — Sync Drive folder ### Studio Content - `studio_create` — Generate audio, video, slides, or infographic - `studio_revise` — Revise slide decks - `download_artifact` — Download generated content ### Research - `research_start` — Web/Drive research - `research_status` — Check research progress - `cross_notebook_query` — Query across multiple notebooks ### Batch & Automation - `batch` — Batch operations (query, create, delete) - `pipeline` — Multi-step workflows - `tag` — Tag and organize notebooks ## Code Examples ### Python: Using the MCP Client ```python from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client # Connect to the MCP server server_params = StdioServerParameters( command="notebooklm-mcp", env=None ) async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() # List notebooks result = await session.call_tool("notebook_list", {}) print(result) # Create notebook result = await session.call_tool("notebook_create", { "title": "Research Project" }) notebook_id = result["id"] # Add source await session.call_tool("source_add", { "notebook": notebook_id, "url": "https://example.com/article" }) # Query result = await session.call_tool("notebook_query", { "notebook": notebook_id, "query": "Summarize the key points" }) print(result) # Generate podcast result = await session.call_tool("studio_create", { "notebook": notebook_id, "content_type": "audio", "confirm": True }) artifact_id = result["artifact_id"] # Download audio_data = await session.call_tool("download_artifact", { "notebook": notebook_id, "artifact_id": artifact_id, "artifact_type": "audio" }) ``` ### Shell Script: Automated Research Pipeline ```bash #!/bin/bash # Create notebook NOTEBOOK_ID=$(nlm notebook create "Daily Research" | jq -r '.id') # Add multiple sources nlm source add "$NOTEBOOK_ID" --url "https://news.ycombinator.com" nlm source add "$NOTEBOOK_ID" --url "https://arxiv.org/list/cs.AI/recent" # Start web research nlm research start "$NOTEBOOK_ID" --query "latest AI breakthroughs" --max-results 20 # Wait for research to complete sleep 60 # Generate podcast nlm studio create "$NOTEBOOK_ID" --type audio --confirm # Get artifact ID ARTIFACT_ID=$(nlm notebook get "$NOTEBOOK_ID" | jq -r '.artifacts[] | select(.type=="audio") | .id' | head -1) # Download nlm download audio "$NOTEBOOK_ID" "$ARTIFACT_ID" --output ./daily-brief.wav # Share publicly nlm share public "$NOTEBOOK_ID" ``` ### Python: Batch Processing ```python import subprocess import json # Tag notebooks for batch processing notebooks = ["nb1", "nb2", "nb3"] for nb in notebooks: subprocess.run(["nlm", "tag", "add", nb, "research", "2024"]) # Batch query result = subprocess.run( ["nlm", "batch", "query", "What are the main themes?", "--tag", "research"], capture_output=True, text=True ) batch_results = json.loads(result.stdout) for nb_id, response in batch_results.items(): print(f"Notebook {nb_id}:") print(response["answer"]) print("---") ``` ### Natural Language with MCP (Claude Code) Once configured, use natural language: ``` User: Create a NotebookLM notebook about quantum computing, add sources from arxiv.org and nature.com, then generate a podcast summarizing the key concepts.