datalab_shopping_keyword_by_age

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

Annotations are absent, so the description must disclose behavioral traits, but it only states that it 'performs a trend analysis.' It does not specify what the output looks like (e.g., time-series data, aggregation method), whether authentication is required, or any rate limits or other constraints. For a data-fetching tool, this is a significant transparency 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 that is front-loaded with the primary action. The Korean translation in parentheses is a minor redundancy but does not detract from clarity. Every word earns its place, and there is no verbose or filler content.

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 6 required parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain the return format, what 'trend analysis' entails, or how the parameters influence the output. An agent would be left uncertain about the consequences of invoking this tool, especially compared to richer descriptions of similar datalab tools.

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 schema description coverage is 100%, providing detailed definitions for all six required parameters, including enum values for ages. The description adds no extra parameter semantics beyond echoing 'by age,' which corresponds to the 'ages' parameter. Therefore, a baseline score of 3 is appropriate, as the schema already carries the weight.

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') and the resource ('Naver Shopping keywords by age'), which is specific and distinguishes it from sibling tools like datalab_shopping_by_age (category-level) and datalab_shopping_keyword_by_device (device-level). The Korean translation reinforces the same meaning, leaving no ambiguity about the tool's purpose.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention that this tool is appropriate for keyword-level trend analysis segmented by age, nor does it exclude cases where other datalab tools (e.g., by gender or device) would be better. The description offers no contextual cues for tool selection.

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