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 full behavioral burden. It discloses the required find_category prerequisite, but says nothing about return format (no output schema), safety profile, rate limits, or how the gender split is computed, leaving meaningful behavioral gaps.

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?

Front-loads the purpose, then adds the prerequisite, business cases, and an example in a scannable order. The bilingual Korean restatement of the example is slightly redundant, keeping it from a 5.

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 five-required-param analytical read with no annotations or output schema, the definition covers purpose, prerequisite, and example adequately. The main gap is the absence of return-value expectations, but full schema coverage compensates for most of that.

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?

With 100% schema description coverage and enum-typed gender/timeUnit, the schema already documents all five parameters. The description reinforces 'gender' and 'category' (via the find_category pointer) but adds no format or constraint detail beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource ('Analyze shopping trends by gender') and disambiguates the demographic dimension (Male vs Female), which separates it from the age/device siblings. It stops short of explicitly naming sibling alternatives like datalab_shopping_by_age, so it is clear but not maximally differentiated.

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?

Gives a concrete prerequisite ('Use find_category first') and lists business cases that establish when to reach for it. It does not state when-not-to-use or name a competing sibling, so it is clear context but lacks explicit exclusions.

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