Notebooklm MCP Secure

🚀 NotebookLM MCP Server (Security Hardened)

Get zero-hallucination answers from NotebookLM + Gemini Deep Research, all with enterprise-grade security.

This repository is a security-hardened fork of PleasePrompto/notebooklm-mcp, maintained by Pantheon Security.

• • • Security • Install

🚀 Quick Start

1. Install (see installation section below)

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]"

✨ Features

• Gemini Deep Research: Access Google's state-of-the-art Deep Research agent via a stable API.
• Document API: Upload and query documents directly via Gemini API, with fast response and auto-chunking for large PDFs.
• Programmatic Notebook Creation: Create, manage, and sync notebooks entirely from code.
• 14 Security Layers: Protect sensitive data with post-quantum encryption, secrets scanning, and more.
• Enterprise Compliance: Meet GDPR, SOC2, and CSSF requirements.

📦 Installation

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"],
      "env": {
        "NLMCP_AUTH_ENABLED": "true",
        "NLMCP_AUTH_TOKEN": "your-secure-token",
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

💻 Usage Examples

Gemini Deep Research

deep_research — Comprehensive Research Agent

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

This command runs Google's Deep Research agent (same as Gemini Advanced), takes 1 - 5 minutes for comprehensive, web-grounded analysis, and returns structured answers with citations and sources.

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)

This tool provides Google Search grounding, code execution, and URL context capabilities, and supports models like gemini-2.5-flash (fast), gemini-2.5-pro (powerful), gemini-3-flash-preview (latest).

get_research_status — Background Task Monitoring

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

Document API

upload_document — Fast Document Upload

Upload /path/to/research-paper.pdf

Supported file types include PDF (50MB, 1000 pages), TXT, MD, HTML, CSV, JSON, DOCX, images, audio, video. It has a 48 - hour retention and 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"

It offers full document understanding and fast API response.

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

Programmatic Notebook Creation

create_notebook — Build Notebooks Instantly

{
  "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"]
}

batch_create_notebooks — Scale Up

{
  "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/" }] }
  ]
}

📚 Documentation

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

See the usage examples section above for details.

Hybrid Architecture

┌──────────────────────────────────────────────────────────────────────────────┐
│                         NotebookLM MCP Server v1.9.0                         │
├──────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│  ┌────────────────────────────────┐    ┌──────────────────────────────────┐  │
│  │      BROWSER AUTOMATION        │    │          GEMINI API              │  │
│  │      (Your Documents)          │    │    (Research & Documents)        │  │
│  ├────────────────────────────────┤    ├──────────────────────────────────┤  │
│  │                                │    │                                  │  │
│  │  QUERY                         │    │  RESEARCH                  v1.8  │  │
│  │  • ask_question                │    │  • deep_research                 │  │
│  │                                │    │  • gemini_query                  │  │
│  │  CREATE & MANAGE         v1.7  │    │  • get_research_status           │  │
│  │  • create_notebook             │    │                                  │  │
│  │  • batch_create_notebooks      │    │  DOCUMENTS (NEW!)          v1.9  │  │
│  │  • manage_sources              │    │  • upload_document               │  │
│  │  • generate_audio              │    │  • query_document                │  │
│  │  • sync_notebook               │    │  • list_documents                │  │
│  │                                │    │  • delete_document               │  │
│  │  Grounded on YOUR docs         │    │                                  │  │
│  │  Permanent storage             │    │  Fast API • 48h retention        │  │
│  └────────────────────────────────┘    └──────────────────────────────────┘  │
│                                                                              │
│                      ┌─────────────────────────────────┐                     │
│                      │       14 SECURITY LAYERS        │                     │
│                      │   Post-Quantum • Audit Logs     │                     │
│                      │   Cert Pinning • Memory Wipe    │                     │
│                      │   GDPR • SOC2 • CSSF Ready      │                     │
│                      └─────────────────────────────────┘                     │
└──────────────────────────────────────────────────────────────────────────────┘

Gemini Configuration

# Required for Gemini features
GEMINI_API_KEY=your-api-key          # Get from https://aistudio.google.com/apikey

# Optional settings
GEMINI_DEFAULT_MODEL=gemini-2.5-flash    # Default model
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

See the usage examples section above for details.

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

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

See the usage examples section above for details.

batch_create_notebooks — Scale Up

See the usage examples section above for details.

manage_sources — Dynamic Source Management

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

generate_audio — Audio Overview Creation

"Generate an audio overview for my Security Research notebook"

sync_notebook — Keep Sources Updated

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

Why This Matters

Your agent can now build entire knowledge bases autonomously.

🔧 Technical Details

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 14 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 (v1.6.0+)

Full compliance support for regulated industries:

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.

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          # Get from https://aistudio.google.com/apikey
GEMINI_DEFAULT_MODEL=gemini-2.5-flash    # Default model
GEMINI_DEEP_RESEARCH_ENABLED=true        # Enable Deep Research
GEMINI_TIMEOUT_MS=30000                  # API timeout

# 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

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

Version History

📄 License

This project is licensed under the MIT License, same as the original.