The only NotebookLM MCP with enterprise-grade security, post-quantum encryption, and full Gemini API integration.

Security-hardened fork of PleasePrompto/notebooklm-mcp • Maintained by Pantheon Security

⚡ TL;DR — What You Get

• 🔍 Query your NotebookLM notebooks — source-grounded, zero-hallucination answers
• 📚 Create & manage notebooks programmatically — no manual clicking
• 🎙️ Generate audio overviews — podcast-style summaries of your docs
• 🎬 Generate video overviews — AI video summaries with 10 visual styles NEW
• 📊 Extract data tables — structured JSON from notebook sources NEW
• 🔬 Gemini 3 + Deep Research — latest models with thinking control (optional API) NEW
• 📄 Document API — upload & query PDFs without browser (optional API)
• 🔐 17 security layers — post-quantum encryption, audit logs, secrets scanning
• ✅ Compliance-ready architecture — built to GDPR, SOC2, and CSSF standards (controls implemented; formal certification requires third-party audit)
• 💡 No API key required — core features work with just browser auth

🚀 What's New in 2026

Latest: v2026.2.10 — 17 security layers, handler architecture overhaul, secure-by-default auth

# Quick install
claude mcp add notebooklm -- npx @pan-sec/notebooklm-mcp@latest

Why Choose This MCP?

Gemini Deep Research (v1.8.0)

The most powerful research capability for AI agents — now in your MCP toolkit.

v1.8.0 introduces the Gemini Interactions API as a stable, API-based research backend alongside browser automation. This gives your agents access to Google's state-of-the-art Deep Research agent.

Why This Matters

New Tools

deep_research — Comprehensive Research Agent

"Research the security implications of post-quantum cryptography adoption in financial services"

• Runs Google's Deep Research agent (same as Gemini Advanced)
• Takes 1-5 minutes for comprehensive, web-grounded analysis
• Returns structured answers with citations and sources
• Perfect for complex topics requiring multi-source synthesis

gemini_query — Fast Grounded Queries

"What are the latest CVEs for Log4j in 2025?" (with Google Search)
"Calculate the compound interest on $10,000 at 5% over 10 years" (with code execution)
"Summarize this security advisory: [URL]" (with URL context)

• Google Search grounding — Current information, not just training data
• Code execution — Run calculations, data analysis
• URL context — Analyze web pages on demand
• Models: gemini-3-flash-preview (default), gemini-3-pro-preview (powerful), gemini-2.5-flash (legacy)

get_research_status — Background Task Monitoring

Run deep research in the background and check progress:

"Start researching [topic] in the background"
... continue other work ...
"Check research status for interaction_abc123"

Hybrid Architecture

┌──────────────────────────────────────────────────────────────────────────────┐
│                      NotebookLM MCP Server v2026.2.x                         │
├──────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│  ┌────────────────────────────────┐    ┌──────────────────────────────────┐  │
│  │      BROWSER AUTOMATION        │    │          GEMINI API              │  │
│  │    ✅ NO API KEY NEEDED        │    │    ⚡ OPTIONAL - needs API key   │  │
│  ├────────────────────────────────┤    ├──────────────────────────────────┤  │
│  │                                │    │                                  │  │
│  │  QUERY                         │    │  RESEARCH                        │  │
│  │  • ask_question                │    │  • deep_research                 │  │
│  │  • get_notebook_chat_history   │    │  • gemini_query                  │  │
│  │                                │    │  • get_research_status           │  │
│  │  CREATE & MANAGE               │    │                                  │  │
│  │  • create_notebook             │    │  DOCUMENTS                       │  │
│  │  • batch_create_notebooks      │    │  • upload_document               │  │
│  │  • manage_sources              │    │  • query_document                │  │
│  │  • generate_audio              │    │  • query_chunked_document        │  │
│  │  • generate_video_overview     │    │  • list/delete_document          │  │
│  │  • generate_data_table         │    │                                  │  │
│  │  • sync_notebook               │    │                                  │  │
│  │                                │    │                                  │  │
│  │  HEALTH & SESSIONS     v2026   │    │                                  │  │
│  │  • get_health (deep_check)     │    │  Fast API • 48h retention        │  │
│  │  • get_query_history           │    │  Auto-chunking for large PDFs    │  │
│  └────────────────────────────────┘    └──────────────────────────────────┘  │
│                                                                              │
│                      ┌─────────────────────────────────┐                     │
│                      │       17 SECURITY LAYERS        │                     │
│                      │   Post-Quantum • Audit Logs     │                     │
│                      │   Cert Pinning • Memory Wipe    │                     │
│                      │  GDPR • SOC2 • CSSF Ready*      │                     │
│                      └─────────────────────────────────┘                     │
└──────────────────────────────────────────────────────────────────────────────┘

💡 Gemini API is completely optional! All core NotebookLM features (ask_question, notebooks, sessions, audio) work via browser automation with no API key required. The Gemini tools below are bonus features for users who want direct API access.

Gemini Configuration (Optional)

# Only required if you want Gemini API features (deep_research, gemini_query, upload_document)
GEMINI_API_KEY=your-api-key          # Get from https://aistudio.google.com/apikey

# Optional settings
GEMINI_DEFAULT_MODEL=gemini-3-flash-preview  # Default model (was gemini-2.5-flash)
GEMINI_DEEP_RESEARCH_ENABLED=true            # Enable Deep Research
GEMINI_TIMEOUT_MS=30000                      # API timeout

When to Use Which

📄 Document API (v1.9.0)

Upload and query documents directly via Gemini API — no browser automation needed.

v1.9.0 introduces the Gemini Files API for fast, reliable document analysis. Upload PDFs, analyze them instantly, and delete when done.

Why This Matters

New Tools

upload_document — Fast Document Upload

Upload any document to Gemini for instant querying:

Upload /path/to/research-paper.pdf

• Supported: PDF (50MB, 1000 pages), TXT, MD, HTML, CSV, JSON, DOCX, images, audio, video
• 48-hour retention — files auto-expire, or delete manually
• Returns a file ID for querying

query_document — Ask Questions About Documents

"What are the main findings in this research paper?"
"Summarize section 3 of the document"
"Extract all statistics mentioned in the PDF"

• Full document understanding (text, tables, charts, diagrams)
• Multi-document queries (compare multiple files)
• Fast API response (no browser wait)

list_documents — See All Uploaded Files

List all my uploaded documents

Shows file names, sizes, MIME types, and expiration times.

delete_document — Clean Up Sensitive Files

Delete file xyz123

Immediately remove files (don't wait for 48h expiration).

Workflow Example

1. upload_document("/research/paper.pdf")
   → Returns: files/abc123

2. query_document("files/abc123", "What methodology was used?")
   → Returns: "The paper uses a mixed-methods approach combining..."

3. query_document("files/abc123", "List all cited authors")
   → Returns: "Smith et al. (2024), Johnson (2023)..."

4. delete_document("files/abc123")
   → File removed

Auto-Chunking for Large PDFs (v1.10.0)

No file size limits — PDFs of any size are automatically handled.

When you upload a PDF that exceeds Gemini's limits (50MB or 1000 pages), the system automatically:

1. Detects the oversized PDF
2. Splits it into optimal chunks (500 pages each)
3. Uploads all chunks in parallel
4. Returns chunk metadata for querying

upload_document("/research/massive-2000-page-report.pdf")

→ Returns:
{
  "wasChunked": true,
  "totalPages": 2000,
  "chunks": [
    { "fileName": "files/abc1", "pageStart": 1, "pageEnd": 500 },
    { "fileName": "files/abc2", "pageStart": 501, "pageEnd": 1000 },
    { "fileName": "files/abc3", "pageStart": 1001, "pageEnd": 1500 },
    { "fileName": "files/abc4", "pageStart": 1501, "pageEnd": 2000 }
  ],
  "allFileNames": ["files/abc1", "files/abc2", "files/abc3", "files/abc4"]
}

query_chunked_document — Query All Chunks at Once

For chunked documents, use this tool to query all parts and get an aggregated answer:

query_chunked_document(
  file_names: ["files/abc1", "files/abc2", "files/abc3", "files/abc4"],
  query: "What are the key recommendations in this report?"
)

→ Queries each chunk, then synthesizes a unified answer

When to Use Document API vs NotebookLM

Programmatic Notebook Creation (v1.7.0+)

Create NotebookLM notebooks entirely from code — no manual clicks required.

Most MCP servers can only read from NotebookLM. This one can create notebooks, add sources, and generate audio — all programmatically.

create_notebook — Build Notebooks Instantly

Create a complete notebook with multiple sources in one command:

{
  "name": "Security Research 2025",
  "sources": [
    { "type": "url", "value": "https://owasp.org/Top10" },
    { "type": "file", "value": "/path/to/security-report.pdf" },
    { "type": "text", "value": "Custom analysis notes...", "title": "My Notes" }
  ],
  "description": "OWASP security best practices",
  "topics": ["security", "owasp", "vulnerabilities"]
}

Supported source types:

• URL — Web pages, documentation, articles
• File — PDF, DOCX, TXT, and more
• Text — Raw text, code snippets, notes

batch_create_notebooks — Scale Up

Create up to 10 notebooks in a single operation:

{
  "notebooks": [
    { "name": "React Docs", "sources": [{ "type": "url", "value": "https://react.dev/reference" }] },
    { "name": "Node.js API", "sources": [{ "type": "url", "value": "https://nodejs.org/api/" }] },
    { "name": "TypeScript Handbook", "sources": [{ "type": "url", "value": "https://www.typescriptlang.org/docs/" }] }
  ]
}

Perfect for:

• Setting up project documentation libraries
• Onboarding new team members with curated knowledge bases
• Creating topic-specific research notebooks in bulk

manage_sources — Dynamic Source Management

Add or remove sources from existing notebooks:

{
  "notebook_id": "abc123",
  "action": "add",
  "sources": [{ "type": "url", "value": "https://new-documentation.com" }]
}

generate_audio — Audio Overview Creation

Generate NotebookLM's famous "Audio Overview" podcasts programmatically:

"Generate an audio overview for my Security Research notebook"

sync_notebook — Keep Sources Updated

Sync notebook sources from a local directory:

{
  "notebook_id": "abc123",
  "directory": "/path/to/docs",
  "patterns": ["*.md", "*.pdf"]
}

Why This Matters

Your agent can now build entire knowledge bases autonomously.

🎬 Video Overviews & Data Tables (v2026.2.0)

Generate AI-powered visual summaries and extract structured data from any notebook — all programmatically.

NotebookLM's Studio panel now offers Video Overviews and Data Tables alongside Audio Overviews. This release brings full automation for both.

generate_video_overview — AI Video Summaries

Generate visual video overviews of your notebook content with 10 visual styles:

Two formats:

• Explainer — Full explanation (5-15 min)
• Brief — Quick summary (1-3 min)

"Generate a kawaii-style brief video overview for my Security Research notebook"

get_video_status — Check Generation Progress

Video generation takes 3-10 minutes. Check progress:

"Check video status for my Security Research notebook"
→ { "status": "generating", "progress": 0 }
→ { "status": "ready" }

generate_data_table — Structured Data Extraction

Generate a structured table from your notebook sources. NotebookLM extracts key information into rows and columns:

"Generate a data table for my research notebook"

get_data_table — Extract Table as JSON

Extract the generated table as structured JSON for processing:

"Get the data table from my research notebook"
→ {
    "headers": ["Title", "Year", "Key Finding", "Methodology", ...],
    "rows": [["Paper A", "2025", "Finding X", "Survey", ...], ...],
    "totalRows": 27,
    "totalColumns": 7
  }

When to Use Which Studio Feature

📊 Query History & Chat Extraction (v2026.1.0)

Track your research and recover conversations from NotebookLM notebooks.

get_query_history — Review Past Research (v1.10.8)

All queries made through the MCP are automatically logged for review:

"Show me my recent NotebookLM queries"
"Find queries about security from last week"
"What did I ask the fine-tuning notebook?"

• Automatic logging — every Q&A pair saved with metadata
• Search — find specific topics across all queries
• Filter — by notebook, session, or date
• Quota tracking — see query counts and timing

get_notebook_chat_history — Extract Browser Conversations (v2026.1.0)

Extract conversation history directly from a NotebookLM notebook's chat UI with context management to avoid overwhelming your AI context window:

Quick audit (preview mode):

{ "notebook_id": "my-research", "preview_only": true }

Returns message counts without content — test the water before extracting.

Export to file (avoids context overflow):

{ "notebook_id": "my-research", "output_file": "/tmp/chat-history.json" }

Dumps full history to disk instead of returning to context.

Paginate through history:

{ "notebook_id": "my-research", "limit": 20, "offset": 0 }
{ "notebook_id": "my-research", "limit": 20, "offset": 20 }

Page through large histories without loading everything at once.

Returns:

{
  "notebook_url": "https://notebooklm.google.com/notebook/xxx",
  "notebook_name": "My Research",
  "total_messages": 150,
  "returned_messages": 40,
  "user_messages": 75,
  "assistant_messages": 75,
  "offset": 0,
  "has_more": true,
  "messages": [...]
}

Use cases:

• Recover conversations made directly in the NotebookLM browser (not tracked by MCP)
• Audit research — see what queries were made in a notebook
• Resume context — pick up where a previous session left off
• Quota reconciliation — understand why quota seems off

Why This Fork?

The original NotebookLM MCP is excellent for productivity — but MCP servers handle sensitive data:

• Browser sessions with Google authentication
• Cookies and tokens stored on disk
• Query history that may contain proprietary information

This fork adds 17 security hardening layers to protect that data.

Security Features

Post-Quantum Ready

Traditional encryption (RSA, ECDH) will be broken by quantum computers. This fork uses hybrid encryption:

ML-KEM-768 (Kyber) + ChaCha20-Poly1305

• ML-KEM-768: NIST-standardized post-quantum key encapsulation
• ChaCha20-Poly1305: Modern stream cipher (immune to timing attacks)

Even if one algorithm is broken, the other remains secure.

Cross-Platform Support

Full native support for all major operating systems:

All sensitive files (encryption keys, auth tokens, audit logs) are automatically protected with owner-only permissions on every platform.

Enterprise Compliance-Ready Architecture (v1.6.0+)

Built to the standards required for regulated industries. All controls are implemented — formal certification (SOC2 Type II report, GDPR registration, CSSF submission) requires a third-party audit engagement.

Compliance Tools (16 MCP tools)

compliance_dashboard    - Real-time compliance status
compliance_report       - Generate audit reports (JSON/CSV/HTML)
compliance_evidence     - Collect evidence packages
grant_consent          - Record user consent
submit_dsar            - Handle data subject requests
request_erasure        - Right to be forgotten
export_user_data       - Data portability export
create_incident        - Security incident management
...and 8 more

See COMPLIANCE-SPEC.md for full documentation.

Installation

What Works Out of the Box (No API Key)

All core NotebookLM features work immediately with just browser authentication:

Optional: Add GEMINI_API_KEY for bonus features like deep_research, gemini_query, and upload_document.

Claude Code

claude mcp add notebooklm -- npx @pan-sec/notebooklm-mcp@latest

With Authentication + Gemini (Recommended)

claude mcp add notebooklm \
  --env NLMCP_AUTH_ENABLED=true \
  --env NLMCP_AUTH_TOKEN=$(openssl rand -base64 32) \
  --env GEMINI_API_KEY=your-gemini-api-key \
  -- npx @pan-sec/notebooklm-mcp@latest

Codex

codex mcp add notebooklm -- npx @pan-sec/notebooklm-mcp@latest

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@pan-sec/notebooklm-mcp@latest"],
      "env": {
        "NLMCP_AUTH_ENABLED": "true",
        "NLMCP_AUTH_TOKEN": "your-secure-token",
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@pan-sec/notebooklm-mcp@latest"]
    }
  }
}

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@pan-sec/notebooklm-mcp@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "notebooklm": {
      "type": "local",
      "command": ["npx", "-y", "@pan-sec/notebooklm-mcp@latest"],
      "enabled": true,
      "environment": {
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@pan-sec/notebooklm-mcp@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

{
  "mcp": {
    "servers": {
      "notebooklm": {
        "command": "npx",
        "args": ["-y", "@pan-sec/notebooklm-mcp@latest"],
        "env": {
          "GEMINI_API_KEY": "your-gemini-api-key"
        }
      }
    }
  }
}

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@pan-sec/notebooklm-mcp@latest"],
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

Quick Start

1. Install (see above)

2. Authenticate

"Log me in to NotebookLM"

Chrome opens → sign in with Google

3. Add your notebook

Go to notebooklm.google.com → Create notebook → Upload docs → Share link

4. Use it

"Research [topic] using this NotebookLM: [link]"

5. Try Deep Research (NEW!)

"Use deep research to investigate [complex topic]"

Complete Tool Reference

Research Tools

Notebook Management

Source Management (v1.7.0+)

Studio Features (v2026.2.0)

Session & System

Compliance (v1.6.0+)

16 compliance tools for GDPR, SOC2, and CSSF requirements.

What Gets Protected

Configuration

All security features are enabled by default. Override via environment variables:

# Authentication
NLMCP_AUTH_ENABLED=true
NLMCP_AUTH_TOKEN=your-secret-token

# Gemini API (v1.8.0+)
GEMINI_API_KEY=your-api-key
GEMINI_DEFAULT_MODEL=gemini-3-flash-preview  # Default (was gemini-2.5-flash)
GEMINI_DEEP_RESEARCH_ENABLED=true
GEMINI_TIMEOUT_MS=30000
NOTEBOOKLM_NO_GEMINI=false       # Set to true to disable all Gemini tools

# Tool Profiles
NOTEBOOKLM_PROFILE=standard      # minimal (5 tools) | standard (33 tools) | full (all 47)
NOTEBOOKLM_DISABLED_TOOLS=       # Comma-separated list of tools to hide

# Encryption
NLMCP_USE_POST_QUANTUM=true
NLMCP_ENCRYPTION_KEY=base64-32-bytes  # Optional custom key

# Session Limits
NLMCP_SESSION_MAX_LIFETIME=28800  # 8 hours
NLMCP_SESSION_INACTIVITY=1800     # 30 minutes

# Secrets Scanning
NLMCP_SECRETS_SCANNING=true
NLMCP_SECRETS_BLOCK=false         # Block on detection
NLMCP_SECRETS_REDACT=true         # Auto-redact

# Certificate Pinning
NLMCP_CERT_PINNING=true

# Audit Logging
NLMCP_AUDIT_ENABLED=true

# Multi-Session Support (v2026.1.2+)
NOTEBOOK_PROFILE_STRATEGY=isolated  # isolated|single|auto
NOTEBOOK_CLONE_PROFILE=true         # Clone auth from base profile

Multi-Session Mode

Run multiple Claude Code sessions simultaneously with isolated browser profiles:

# Add to ~/.bashrc or ~/.zshrc
export NOTEBOOK_PROFILE_STRATEGY=isolated
export NOTEBOOK_CLONE_PROFILE=true

How it works:

• Each session gets its own Chrome profile (no lock conflicts)
• Isolated profiles clone from the authenticated base profile
• Auth coordination ensures cloning waits for any in-progress authentication

See SECURITY.md for complete configuration reference.

Security Scanning

Run MEDUSA security scanner:

npm run security-scan

Or integrate in CI/CD:

- name: Security Scan
  run: npx @pan-sec/notebooklm-mcp && npm run security-scan

Comparison

vs Other NotebookLM MCPs

Security & Compliance (Unique to This Fork)

* Compliance-ready: all required controls are implemented. Formal certification (SOC2 Type II report, GDPR registration, CSSF submission) requires a third-party audit engagement.

Bottom line: If you need more than basic queries, or care about security, there's only one choice.

Version History

Reporting Vulnerabilities

Found a security issue? Do not open a public GitHub issue.

Email: support@pantheonsecurity.io

Credits

• Original MCP Server: Gérôme Dexheimer — notebooklm-mcp
• Security Hardening: Pantheon Security
• Post-Quantum Crypto: @noble/post-quantum
• Gemini API: Google AI

License

MIT — Same as original.