datalab_shopping_keyword_by_age

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 only states what the tool analyzes but does not disclose how the results are presented, what age groups are included, aggregation behavior, or any limitations. The description adds little beyond the tool's name.

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 a single, concise sentence that front-loads the main purpose. There is no repetition or unnecessary detail.

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 7 parameters, no output schema, and no annotations, the description is too sparse. It does not explain the output format, expected input ranges, or how the analysis is scoped. It provides only a high-level statement, leaving significant gaps for an agent to use the tool correctly.

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 schema covers only 57% of parameters, so the description needs to compensate, but it does not. It adds no explanation for device, gender, timeUnit, category, or keyword beyond what the schema already states. The description's phrase 'specific category keyword' is vague and does not clarify parameter usage.

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 analyzes click ratios by age group for a specific category keyword, with a specific verb ('analyzes') and resource ('shopping keyword click ratio'). The age-specific scope distinguishes it from sibling tools like datalab_shopping_keyword_by_gender and datalab_shopping_keyword_by_device.

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 gives no guidance on when to use this tool versus alternatives. It does not mention that this tool is for age breakdowns as opposed to gender/device breakdowns, nor any prerequisites or scenarios where it should be preferred.

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