ask_notebook

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

No annotations are provided, so the description carries full burden. It mentions the action ('ask a question') and outcome ('AI-generated answer'), but lacks behavioral details such as authentication needs, rate limits, response format, or error handling. For a tool that likely involves AI processing, this is a significant gap in transparency.

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?

The description is appropriately sized and front-loaded: the first sentence clearly states the purpose, followed by a concise Args section. There's no wasted text, and the structure aids readability. A slight deduction because the Args formatting could be more integrated, but overall it's efficient.

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 2 parameters with 0% schema coverage, no annotations, and an output schema (which reduces need to explain returns), the description is moderately complete. It covers the basic action and parameters but misses behavioral context (e.g., how answers are generated, limitations). For a simple Q&A tool, this is adequate but has clear gaps.

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?

Schema description coverage is 0%, so the schema provides no parameter details. The description adds minimal semantics: it names the parameters (notebook_id, query) and briefly describes them ('The ID of the NotebookLM notebook', 'The question to ask in natural language'). This compensates somewhat but doesn't fully address format expectations (e.g., notebook_id source, query length limits), resulting in a baseline score.

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 tool's purpose: 'Ask a question to a specific NotebookLM notebook and get an AI-generated answer.' It specifies the verb ('ask'), resource ('NotebookLM notebook'), and outcome ('AI-generated answer'), distinguishing it from siblings like list_notebooks or get_notebook_sources. However, it doesn't explicitly differentiate from other AI interaction tools like generate_report, which slightly reduces specificity.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a notebook_id from list_notebooks), exclusions (e.g., not for editing), or comparisons to siblings like generate_report (which might produce structured outputs). Usage is implied by the purpose but lacks explicit context for selection.

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