datalab_shopping_by_gender

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 mentions 'trend analysis' but doesn't specify what data is returned (e.g., time-series metrics, aggregated statistics), whether it's a read-only operation, any rate limits, or authentication requirements. For a data analysis tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 concise and front-loaded with the core purpose in the first sentence. The Korean translation adds minimal redundancy. However, it could be slightly more structured by explicitly mentioning the required parameters or output format.

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's complexity (5 required parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks details on behavioral traits, output format, and usage context. With no output schema, the agent doesn't know what data to expect, which is a significant gap for a trend analysis tool.

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%, with all parameters clearly documented in the schema (e.g., 'Category code', 'End date (yyyy-mm-dd)'). The description adds no additional parameter semantics beyond implying gender-based filtering. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 purpose: 'Perform a trend analysis on Naver Shopping by gender.' It specifies the verb ('perform trend analysis'), resource ('Naver Shopping'), and dimension ('by gender'). However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_by_device' beyond the gender focus.

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. It doesn't mention sibling tools like 'datalab_shopping_by_age' for age-based analysis or 'datalab_shopping_keyword_by_gender' for keyword-focused gender analysis. The agent must infer usage from the name and description alone.

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