datalab_shopping_by_age

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention whether the operation is read-only, any authentication or rate-limit requirements, or what the output contains. The phrase 'trend analysis' only vaguely implies a data-returning operation.

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, front-loaded sentence that conveys the core purpose immediately. The parenthetical Korean translation is mildly redundant with the English but does not harm clarity or add meaningful length.

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?

With no output schema and no annotations, the description does not explain the return format or any additional constraints. However, the schema fully covers required parameters and the tool's purpose is simple, making the description minimally viable but not comprehensive.

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 input schema already documents all 5 parameters with descriptions and enums, giving 100% schema coverage. The description adds no parameter-level detail beyond the schema, so it meets the baseline but does not enhance parameter understanding.

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 action ('perform a trend analysis'), the resource ('Naver Shopping'), and the segmentation ('by age'), which distinguishes it from sibling tools like datalab_shopping_by_device or datalab_shopping_by_gender. The verb 'Perform' is generic, but the resource and scope make the purpose unambiguous.

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 does not provide any explicit guidance on when to use this tool versus alternatives. It lacks mentions of excluded cases, prerequisites, or sibling tools, leaving the agent to infer usage solely from the 'by age' phrase.

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