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notebook.delete

Destructive

Remove unused NotebookLM notebooks and free up space by providing an array of notebook IDs; returns deleted and failed lists for retry or reporting.

Instructions

Delete one or more notebooks directly from NotebookLM (UI-level deletion, not just from the local library). Pass an array of notebook IDs (UUIDs from list_notebooks_from_nblm).

Returns { deleted: [...], failed: [...] } so the caller can retry or report on partial failures.

Use this to:

  • Bulk-clean up a NotebookLM account (e.g. test notebooks from automation runs)

  • Free up the 100-notebook free-tier quota

  • Remove notebooks no longer covered by your sources

Warning: This is irreversible at the NotebookLM side. Confirm with the user before calling. Note: Requires authentication. Run setup_auth first if not authenticated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notebook_idsYesArray of NotebookLM notebook IDs (UUIDs) to delete. Use `list_notebooks_from_nblm` to discover them.
show_browserNoShow browser window during deletion. Default: false (headless).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successYesWhether the tool call succeeded.
dataNoThe tool payload on success. The exact shape depends on the tool.
errorNoHuman-readable error message, present only when success is false.
Behavior5/5

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

The description goes beyond annotations by stating the deletion is irreversible at the NotebookLM side, that it returns partial failure details, and that authentication is required. This provides rich behavioral context that annotations alone do not cover.

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 concise and well-structured: a clear first sentence, followed by return format, bullet-pointed use cases, a warning, and a note. Every sentence adds value with no redundancy.

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 has only two parameters and clear annotations, the description fully covers behavior, return format, use cases, warnings, and prerequisites. The output schema is implied by the described return structure, making it complete.

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 coverage is 100%, and the description does not add significant meaning beyond the schema descriptions for either parameter. The baseline score of 3 is appropriate.

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 the tool deletes one or more notebooks from NotebookLM, distinguishing it from local library deletion. It references the sibling tool `list_notebooks_from_nblm` for discovering notebook IDs, making its purpose unambiguous.

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 explicitly lists three use cases (bulk cleanup, freeing quota, removing uncovered notebooks) and includes a warning to confirm with the user. However, it does not explicitly state when not to use this tool or mention alternative tools for similar tasks.

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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