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notebook_list

Retrieve and display all available notebooks in NotebookLM, allowing users to view and manage their existing projects with customizable result limits.

Instructions

List all notebooks.

Args: max_results: Maximum number of notebooks to return (default: 100)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a list operation, implying it's read-only, but doesn't confirm this or mention other traits like rate limits, pagination behavior (beyond the max_results parameter), or what happens if no notebooks exist. The description lacks details on return format, error conditions, or performance characteristics.

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 extremely concise and well-structured. The first sentence 'List all notebooks.' front-loads the core purpose clearly. The 'Args:' section efficiently documents the parameter without unnecessary elaboration. Every sentence earns its place, with zero wasted words or redundant information.

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

Completeness3/5

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

Given the tool's low complexity (a simple list operation with one optional parameter) and the presence of an output schema (which handles return values), the description is minimally complete. It covers the purpose and parameter semantics adequately. However, for a tool with no annotations, it should ideally include more behavioral context (e.g., read-only nature, pagination hints) to fully guide an agent, leaving some gaps in completeness.

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

Parameters4/5

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

The description adds meaningful context for the single parameter 'max_results' by explaining it's the maximum number of notebooks to return and providing its default value (100). Since schema description coverage is 0% (the schema only defines the type and default without a description), the description fully compensates by clarifying the parameter's purpose and default behavior, making it easy for an agent to use correctly.

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

Purpose4/5

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

The description clearly states the verb 'List' and the resource 'all notebooks', making the purpose immediately understandable. It distinguishes from siblings like notebook_create, notebook_describe, and notebook_query by focusing on listing all notebooks without filtering or querying. However, it doesn't explicitly differentiate from mind_map_list, which is a similar listing operation for a different resource.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose notebook_list over notebook_query (which likely allows filtering) or notebook_get (which retrieves a specific notebook). There's no context about prerequisites, such as whether authentication is needed or if it works with all notebook types.

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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