datalab_shopping_by_gender

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only mentions 'trend analysis,' which implies a read operation, but does not specify output format, data granularity, limitations, or any side effects. This is a minimal disclosure gap.

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 in English and Korean, with no wasted words. It lacks structural elements like bullet points, but it is appropriately sized for a simple tool.

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 has five required parameters, no output schema, and no annotations, the description should provide more context about the analysis results, return values, or use cases. The generic 'trend analysis' phrasing leaves significant gaps for an agent to know what to expect.

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 has 100% parameter coverage with descriptions for all five required fields. The description adds no additional meaning beyond the schema, so the baseline of 3 is appropriate.

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 performs a trend analysis on Naver Shopping, specifically segmented by gender. The verb 'perform' and resource 'Naver Shopping trend analysis' are specific, and the 'by gender' modifier differentiates it from sibling tools like by_age or 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 provides no guidance on when to use this tool versus alternatives. With many similar datalab_shopping_* siblings, the lack of any usage context or exclusions leaves the agent without direction for selecting this tool.

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