datalab_shopping_keyword_by_gender

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only states the high-level action. It does not mention what the response contains, whether results are aggregated by time or gender, or any limitations such as date range constraints or data source quirks. The Korean translation adds no additional behavioral insight.

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, front-loaded sentence with no redundant words. It communicates the core purpose efficiently. The Korean parenthetical is optional localization but does not detract from conciseness.

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?

The tool has six required parameters and no output schema or annotations. The description alone provides only the high-level purpose, which is sufficient to guess what the tool does but not enough to understand how parameters interact or what the result will look like. Given the presence of many similar sibling tools, more context about this tool's specific role would be needed for full completeness.

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 100%, so the schema already documents all parameters. The description does not add any extra meaning beyond the schema, such as how gender values map to 'female'/'male' or how category codes are used. It meets the baseline for complete schema coverage but provides no further value.

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 function: 'Perform a trend analysis on Naver Shopping keywords by gender.' It names the specific resource (Naver Shopping keywords), the action (trend analysis), and the grouping dimension (gender). This distinguishes it from sibling tools like datalab_shopping_keyword_by_device and datalab_shopping_keyword_by_age.

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 alternatives. The description does not mention exclusions, prerequisites, or comparison with siblings such as datalab_shopping_by_gender. The usage context must be inferred entirely from the tool name and schema.

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