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

v2026.1.1 brings powerful new capabilities:

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

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.

"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

"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-2.5-flash (fast), gemini-2.5-pro (powerful), gemini-3-flash-preview (latest)

Run deep research in the background and check progress:

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

┌──────────────────────────────────────────────────────────────────────────────┐
│                      NotebookLM MCP Server v2026.1.x                         │
├──────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│  ┌────────────────────────────────┐    ┌──────────────────────────────────┐  │
│  │      BROWSER AUTOMATION        │    │          GEMINI API              │  │
│  │      (Your Documents)          │    │    (Research & Documents)        │  │
│  ├────────────────────────────────┤    ├──────────────────────────────────┤  │
│  │                                │    │                                  │  │
│  │  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        │  │
│  │  • sync_notebook               │    │  • list/delete_document          │  │
│  │                                │    │                                  │  │
│  │  HEALTH & SESSIONS     v2026   │    │                                  │  │
│  │  • get_health (deep_check)     │    │  Fast API • 48h retention        │  │
│  │  • get_query_history           │    │  Auto-chunking for large PDFs    │  │
│  └────────────────────────────────┘    └──────────────────────────────────┘  │
│                                                                              │
│                      ┌─────────────────────────────────┐                     │
│                      │       14 SECURITY LAYERS        │                     │
│                      │   Post-Quantum • Audit Logs     │                     │
│                      │   Cert Pinning • Memory Wipe    │                     │
│                      │   GDPR • SOC2 • CSSF Ready      │                     │
│                      └─────────────────────────────────┘                     │
└──────────────────────────────────────────────────────────────────────────────┘

# 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

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.

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

"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 all my uploaded documents

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

Delete file xyz123

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

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

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"]
}

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

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 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

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

Add or remove sources from existing notebooks:

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

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

"Generate an audio overview for my Security Research notebook"

Sync notebook sources from a local directory:

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

Your agent can now build entire knowledge bases autonomously.

Track your research and recover conversations from NotebookLM notebooks.

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

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

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.

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.

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.

Full compliance support for regulated industries:

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.

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

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 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"
      }
    }
  }
}

"Log me in to NotebookLM"

Chrome opens → sign in with Google

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

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

"Use deep research to investigate [complex topic]"

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

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-2.5-flash
GEMINI_DEEP_RESEARCH_ENABLED=true
GEMINI_TIMEOUT_MS=30000

# 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.

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

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

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

Email: [email protected]

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

MIT — Same as original.