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batch

Perform batch operations across multiple NotebookLM notebooks: query, add sources, create, delete, or generate studio artifacts.

Instructions

Perform batch operations across multiple notebooks.

Actions:

  • query: Query multiple notebooks with the same question

  • add_source: Add the same source URL to multiple notebooks

  • create: Create multiple notebooks at once

  • delete: Delete multiple notebooks (IRREVERSIBLE, requires confirm=True)

  • studio: Generate studio artifacts across multiple notebooks

Args: action: Operation to perform (query, add_source, create, delete, studio) query: Question to ask (for action=query) source_url: URL to add (for action=add_source) titles: Comma-separated notebook titles (for action=create) artifact_type: Artifact type (for action=studio): audio, video, report, etc. notebook_names: Comma-separated notebook names or IDs tags: Comma-separated tags to select notebooks all: Apply to ALL notebooks confirm: Must be True for delete action

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
allNo
tagsNo
queryNo
actionYes
titlesNo
confirmNo
source_urlNo
artifact_typeNoaudio
notebook_namesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: it lists actions like delete as irreversible and requiring confirm=True. It also describes each action's purpose. The existence of an output schema covers return values, so no further elaboration needed.

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

Conciseness4/5

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

The description is well-structured: a concise opening summary, then a bulleted list of actions, then parameter explanations. It is slightly lengthy but efficiently organized, front-loading the purpose.

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

Completeness5/5

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

Given the tool's complexity (9 parameters, multiple actions), the description covers actions, parameter usage, and notable constraints (e.g., confirm for delete). The presence of an output schema fills in return structure, making it complete for agent invocation.

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

Parameters5/5

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

Schema description coverage is 0%, but the description provides meaningful explanations for each parameter (e.g., query for action=query, source_url for add_source, notebook_names as comma-separated strings). This adds value beyond the raw schema.

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 clearly states it performs batch operations across multiple notebooks, listing specific actions. It distinguishes itself from sibling tools like notebook_create, notebook_delete, and source_add by being a batch operation tool.

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

Usage Guidelines3/5

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

The description provides clear usage context within the tool (e.g., actions and their parameters). However, it does not explicitly state when to use this tool versus alternative individual tools (e.g., notebook_create for single notebook creation), which would help an agent decide between siblings.

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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