ask_notebook

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

No annotations are provided, so the description fully bears the transparency burden. It describes the tool as a read operation (get answer), non-destructive. However, it lacks details on authentication, rate limits, or the exact format of the answer. The output schema may compensate, but the description itself is minimal.

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

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

Extremely concise and well-structured: one sentence for purpose followed by a numbered list for parameters. No unnecessary words.

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

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

Given the tool's simplicity (2 required params, no enums, output schema present), the description is complete. It explains what the tool does, the parameters needed, and how to get the notebook ID. The output schema likely covers return value details.

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

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

The description adds valuable context beyond the schema by specifying that notebook_id is obtained via list_notebooks. The question parameter is simply restated. Schema coverage is 0%, so this addition is significant.

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

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

The description clearly states the verb 'Ask a question' and the resource 'specific NotebookLM notebook', with a clear output 'get an AI answer based on its sources'. It distinguishes from siblings like list_notebooks, find_notebook, and generate_quiz by focusing on querying a notebook for answers.

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

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

The description implicitly guides usage by telling the user to use list_notebooks to find notebook IDs, but does not explicitly state when to use this tool vs alternatives or provide exclusions.

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