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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description only needs to add behavioral context. It adds that the tool generates AI insights and supports batch execution, but does not explain what happens with the summary (e.g., whether it is cached, any limits).

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 very short (two sentences) with no fluff. It front-loads the purpose and then adds bulk support. However, it could be slightly more structured by listing parameters explicitly.

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?

For a tool with 3 parameters and no output schema, the description lacks key details: it does not explain the 'notebook_id' parameter, the expected output format (e.g., plain text or structured JSON), or any limitations. Despite annotations covering safety, the description is incomplete.

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 description must explain all parameters. It only describes 'topics' (boolean for including topics) and 'notebook_ids' (bulk support), but fails to clarify the required 'notebook_id' parameter, leaving a critical gap.

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 states it generates a 'notebook summary with AI-generated insights' and mentions topics and bulk support. The purpose is clear as a summary retrieval tool, but it could be more precise about the exact output (e.g., text summary vs structured data).

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?

No guidance is provided on when to use this tool versus sibling tools like notebooklm_notebook_metadata or notebooklm_notebook_list. The only usage hint is that 'topics:true' includes suggested topics, but no exclusions or alternatives are mentioned.

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