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Pantheon-Security

NotebookLM MCP Server (Security Hardened)

Batch Create

batch_create_notebooks

Create multiple NotebookLM notebooks simultaneously to organize research materials efficiently, with options for error handling and progress tracking.

Instructions

Create multiple NotebookLM notebooks in one operation.

What This Tool Does

  • Creates up to 10 notebooks in a single batch operation

  • Reports progress for each notebook

  • Optionally continues on error or stops on first failure

  • Auto-adds created notebooks to your library

Example Usage

{
  "notebooks": [
    {
      "name": "React Documentation",
      "sources": [
        { "type": "url", "value": "https://react.dev/reference" }
      ],
      "topics": ["react", "frontend"]
    },
    {
      "name": "Node.js API",
      "sources": [
        { "type": "url", "value": "https://nodejs.org/api/" }
      ],
      "topics": ["nodejs", "backend"]
    }
  ],
  "stop_on_error": false
}

Limits

  • Maximum 10 notebooks per batch

  • Each notebook follows individual source limits (50-600 based on tier)

  • Delays between notebooks to avoid rate limiting

Returns

Summary with:

  • total: Number of notebooks attempted

  • succeeded: Successfully created count

  • failed: Failed count

  • results: Array of individual results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notebooksYesArray of notebooks to create (max 10)
stop_on_errorNoStop batch if any notebook fails (default: false)
show_browserNoShow browser window for debugging
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate non-read-only, non-destructive, non-idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations: it explains progress reporting, error handling options (stop_on_error), auto-addition to library, delays for rate limiting, and return summary structure. This enriches understanding without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (What This Tool Does, Example Usage, Limits, Returns), each sentence adds value (e.g., explaining batch limits, error handling, returns), and it avoids redundancy. It is appropriately sized and front-loaded with key information, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (batch creation with multiple parameters) and lack of output schema, the description does a good job covering behavior, limits, and return values. However, it could improve by explicitly mentioning authentication needs or linking to sibling tools for context, slightly reducing completeness for a mutation tool without output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema, such as implying 'notebooks' array structure through the example and mentioning 'stop_on_error' behavior. This meets the baseline for high schema coverage but doesn't significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool 'creates multiple NotebookLM notebooks in one operation,' clearly specifying the verb (create) and resource (notebooks). It distinguishes from sibling tools like 'create_notebook' by emphasizing batch capability and up to 10 notebooks, making the purpose specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (creating multiple notebooks efficiently) and mentions limits like maximum 10 notebooks, which implies when not to use it for larger batches. However, it does not explicitly name alternatives (e.g., 'create_notebook' for single creation) or detail prerequisites, keeping it from a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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