notebooklm-mcp-cli

NotebookLM MCP CLI

Skill by ara.so — Devtools Skills collection.

Overview

NotebookLM MCP CLI provides programmatic access to Google NotebookLM through:

CLI (nlm): Direct terminal commands for scripting and automation
MCP Server (notebooklm-mcp): Model Context Protocol server for AI agents (Claude, Gemini, Cursor, etc.)

Both interfaces access the same NotebookLM API capabilities: notebook management, source addition, audio/video generation, research automation, and sharing.

Important: Uses undocumented internal APIs requiring browser cookie extraction. Supports NotebookLM Pro/free and Google AI Ultra accounts.

Installation

# Recommended: Install with uv
uv tool install notebooklm-mcp-cli

Alternative: pip

pip install notebooklm-mcp-cli

Alternative: pipx

pipx install notebooklm-mcp-cli

Verify installation

nlm --version


This installs both:
- `nlm` - CLI interface
- `notebooklm-mcp` - MCP server

## Authentication

### Initial Setup

```bash
# Auto mode: launches browser for cookie extraction
nlm login

# Check authentication status
nlm login --check

# Manual mode with cookie file
nlm login --manual --file cookies.txt

Profile Management (Multiple Google Accounts)

# Create named profiles
nlm login --profile work
nlm login --profile personal

# Switch profiles
nlm login switch work

# List all profiles
nlm login profile list

# Delete a profile
nlm login profile delete personal

Browser Selection

# Set preferred browser
nlm config set auth.browser brave

# Supported: chrome, arc, brave, edge, chromium, firefox

CLI Usage

Notebook Management

# List all notebooks
nlm notebook list

# Create a new notebook
nlm notebook create "Research Project"

# Get notebook details
nlm notebook get <notebook-id>

# Delete a notebook
nlm notebook delete <notebook-id>

# Rename a notebook
nlm notebook rename <notebook-id> "New Name"

Adding Sources

# Add URL source
nlm source add <notebook-id> --url "https://example.com"

# Add text source
nlm source add <notebook-id> --text "Your content here" --title "Notes"

# Add Google Drive file
nlm source add <notebook-id> --drive-id "1ABC..." --title "Document"

# Add local file (uploads to Drive first)
nlm source add <notebook-id> --file ./document.pdf

# Add multiple sources at once
nlm source add <notebook-id> \
  --url "https://site1.com" \
  --url "https://site2.com" \
  --text "Summary notes"

# List sources in notebook
nlm source list <notebook-id>

# Delete a source
nlm source delete <notebook-id> <source-id>

Querying Notebooks

# Ask a question (persists to web UI)
nlm notebook query <notebook-id> "What are the key findings?"

# Query with custom settings
nlm notebook query <notebook-id> "Summarize this" \
  --grounding "Always cite sources" \
  --format-as markdown

Studio Content Creation

# Create audio podcast (Deep Dive)
nlm studio create <notebook-id> --type audio --confirm

# Create video presentation
nlm studio create <notebook-id> --type video --confirm

# Create slides
nlm studio create <notebook-id> --type slides --confirm

# Interactive mode (prompts for confirmation)
nlm studio create <notebook-id> --type audio

# List studio artifacts
nlm studio list <notebook-id>

Revising Slides

# Revise specific slides with instructions
nlm slides revise <notebook-id> <artifact-id> \
  --slides 1,3,5 \
  --instruction "Add more technical details and code examples"

# Revise all slides
nlm slides revise <notebook-id> <artifact-id> \
  --instruction "Make the tone more casual"

# Check revision status
nlm slides status <notebook-id> <revision-id>

Downloading Artifacts

# Download audio file
nlm download audio <notebook-id> <artifact-id> --output podcast.wav

# Download video
nlm download video <notebook-id> <artifact-id> --output presentation.mp4

# Download slides as PDF
nlm download slides <notebook-id> <artifact-id> --output deck.pdf

# Auto-generate filename
nlm download audio <notebook-id> <artifact-id>

Sharing

# Enable public link
nlm share public <notebook-id>

# Invite specific email
nlm share invite <notebook-id> user@example.com

# Get share status
nlm share status <notebook-id>

# Revoke public link
nlm share revoke <notebook-id>

Research Automation

# Start web research with queries
nlm research start <notebook-id> \
  --query "quantum computing breakthroughs 2024" \
  --query "quantum error correction"

# Drive-based research
nlm research drive <notebook-id> \
  --drive-id "1ABC..." \
  --query "financial trends"

# Check research status
nlm research status <notebook-id> <research-id>

Batch Operations

# Batch query across notebooks
nlm batch query "What are the main themes?" \
  --notebooks <id1> <id2> <id3>

# Batch create notebooks
nlm batch create \
  --names "Project A" "Project B" "Project C"

# Batch delete
nlm batch delete --notebooks <id1> <id2> <id3> --confirm

Cross-Notebook Queries

# Query across multiple notebooks
nlm cross query "Compare the methodologies" \
  --notebooks <id1> <id2> <id3>

# Smart notebook selection by tags
nlm cross query "What are common themes?" \
  --tags research papers

Tagging

# Add tags to notebook
nlm tag add <notebook-id> research important

# List all tags
nlm tag list

# Select notebooks by tag
nlm tag select research

Pipelines (Multi-Step Workflows)

# Run a predefined pipeline
nlm pipeline run research-to-podcast \
  --notebook <notebook-id> \
  --urls "https://site1.com,https://site2.com"

# List available pipelines
nlm pipeline list

# Create custom pipeline (YAML)
cat > my-pipeline.yaml <<EOF
name: research-to-podcast
steps:
  - type: source_add
    urls: {{ urls }}
  - type: studio_create
    content_type: audio
EOF

nlm pipeline run my-pipeline.yaml --notebook <id> --urls "https://..."

MCP Server Setup

Automatic Configuration

# Add to AI tools automatically
nlm setup add claude-code
nlm setup add claude-desktop
nlm setup add gemini
nlm setup add github-copilot
nlm setup add cursor
nlm setup add windsurf
nlm setup add cline
nlm setup add antigravity

# Generate JSON config for custom tools
nlm setup add json

# List configured tools
nlm setup list

# Remove from a tool
nlm setup remove claude-code

Manual MCP Configuration

If automatic setup doesn't work, manually add to your MCP client config:

{
  "mcpServers": {
    "notebooklm-mcp": {
      "command": "notebooklm-mcp",
      "args": [],
      "env": {
        "NOTEBOOKLM_PROFILE": "default"
      }
    }
  }
}

Config file locations:

Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
Cursor: ~/Library/Application Support/Cursor/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json
Gemini CLI: ~/.config/gemini-ai/mcp.json

Install AI Skills (Optional)

# Install expert guide for your AI assistant
nlm skill install cline
nlm skill install claude-code
nlm skill install gemini

# Update skills
nlm skill update cline

# List installed skills
nlm skill list

MCP Tools Reference

Core Tools

# List notebooks
notebook_list()

# Create notebook
notebook_create(title="Research Project")

# Add sources
source_add(
    notebook_id="abc123",
    urls=["https://example.com"],
    texts=[{"content": "Notes", "title": "Summary"}],
    drive_ids=["1ABC..."],
    files=["/path/to/file.pdf"]
)

# Query notebook
notebook_query(
    notebook_id="abc123",
    query="What are the key findings?",
    grounding_instruction="Cite sources",
    format_as="markdown"
)

# Create studio content
studio_create(
    notebook_id="abc123",
    content_type="audio",  # or "video", "slides"
    confirm=True
)

# Download artifact
download_artifact(
    notebook_id="abc123",
    artifact_id="xyz789",
    artifact_type="audio",  # or "video", "slides"
    output_path="podcast.wav"
)

# Share notebook
notebook_share_public(notebook_id="abc123")
notebook_share_invite(notebook_id="abc123", email="user@example.com")

Advanced Tools

# Research automation
research_start(
    notebook_id="abc123",
    queries=["topic 1", "topic 2"],
    source_type="web"  # or "drive"
)

# Batch operations
batch_query(
    query="Compare approaches",
    notebook_ids=["id1", "id2", "id3"]
)

# Cross-notebook query
cross_notebook_query(
    query="What are common patterns?",
    notebook_ids=["id1", "id2"],
    tags=["research"]
)

# Pipeline execution
pipeline_run(
    pipeline_name="research-to-podcast",
    notebook_id="abc123",
    params={"urls": ["https://site.com"]}
)

# Slide revision
studio_revise(
    notebook_id="abc123",
    artifact_id="xyz789",
    instruction="Add more technical details",
    slide_numbers=[1, 3, 5]
)

Common Workflows

Research Paper to Podcast

#!/bin/bash
# Create notebook
NOTEBOOK_ID=$(nlm notebook create "Research Summary" | jq -r '.id')

# Add sources
nlm source add $NOTEBOOK_ID \
  --url "https://arxiv.org/pdf/2024.12345.pdf" \
  --url "https://related-paper.com"

# Generate podcast
nlm studio create $NOTEBOOK_ID --type audio --confirm

# Download when ready (check status first)
sleep 60  # Wait for generation
ARTIFACT_ID=$(nlm studio list $NOTEBOOK_ID | jq -r '.[0].id')
nlm download audio $NOTEBOOK_ID $ARTIFACT_ID --output research.wav

Automated Weekly Research Digest

#!/usr/bin/env python3
import subprocess
import json

# Create notebook
result = subprocess.run(
    ["nlm", "notebook", "create", "Weekly Digest"],
    capture_output=True, text=True
)
notebook_id = json.loads(result.stdout)["id"]

# Add URLs from research tracking
urls = [
    "https://news.ycombinator.com/best",
    "https://paperswithcode.com/latest",
]

subprocess.run([
    "nlm", "source", "add", notebook_id,
    *[f"--url={url}" for url in urls]
])

# Query for summary
subprocess.run([
    "nlm", "notebook", "query", notebook_id,
    "Summarize the key developments this week"
])

# Generate video presentation
subprocess.run([
    "nlm", "studio", "create", notebook_id,
    "--type=video", "--confirm"
])

Multi-Notebook Cross-Analysis

# Tag related notebooks
nlm tag add nb1 quarterly-review finance
nlm tag add nb2 quarterly-review finance
nlm tag add nb3 quarterly-review finance

# Query across all tagged notebooks
nlm cross query "Compare revenue growth patterns" --tags quarterly-review finance

# Or specify exact notebooks
nlm cross query "What are the common risks?" --notebooks nb1 nb2 nb3

Configuration

# View all settings
nlm config list

# Set auth browser preference
nlm config set auth.browser brave

# Set default output directory
nlm config set download.output_dir ~/Downloads/notebooklm

# Enable debug logging
nlm config set logging.level debug

# Reset to defaults
nlm config reset

Troubleshooting

Diagnose Issues

# Run comprehensive diagnostics
nlm doctor

# Check specific components
nlm login --check
nlm setup list

Common Issues

Authentication expired:

nlm login --force  # Re-authenticate

MCP server not responding:

# Restart your AI tool (Claude Code, Cursor, etc.)
# Or reconnect: /mcp (in Claude Code)

Rate limiting:

# NotebookLM has rate limits. Space out requests:
nlm studio create <id> --type audio
sleep 30
nlm studio create <id2> --type audio

Profile switching:

# Switch between Google accounts
nlm login switch work
nlm login switch personal

Debug Logging

# Enable verbose output
nlm --debug notebook list

# Check logs
tail -f ~/.notebooklm-mcp-cli/logs/nlm.log

Best Practices

Use profiles for multiple Google accounts to avoid re-authenticating
Tag notebooks for easier cross-notebook queries and organization
Wait for studio content - audio/video generation takes 3-5 minutes
Batch operations for efficiency when working with multiple notebooks
Disable MCP when not using NotebookLM to preserve AI context window
Use pipelines for repeatable multi-step workflows
Check status before downloading - artifacts may still be generating

Environment Variables

# Use specific profile
export NOTEBOOKLM_PROFILE=work

# Custom config directory
export NOTEBOOKLM_CONFIG_DIR=~/.config/notebooklm

# Debug mode
export NOTEBOOKLM_DEBUG=1

Limitations

Uses internal APIs that may change without notice
Requires cookie extraction from browser session
Rate limits apply (space out studio content creation)
No official support from Google - personal/experimental use only
Context window - 35 MCP tools consume significant context in AI assistants

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