datalab_shopping_keyword_by_gender

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'trend analysis' implies a read-only operation, the description doesn't specify whether this requires authentication, has rate limits, returns time-series data, or what format the analysis takes. For a tool with 6 required parameters and no output schema, this leaves significant behavioral questions unanswered.

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 appropriately concise with two parallel statements (English and Korean) that convey the same essential information. While the Korean translation adds redundancy, it doesn't significantly detract from efficiency. The description is front-loaded with the core purpose and wastes no space on unnecessary details.

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 (6 required parameters, trend analysis function) and lack of both annotations and output schema, the description is insufficiently complete. It doesn't explain what the analysis returns, how results are structured, or provide context about the Naver Shopping platform. The agent must rely entirely on the parameter schema without understanding the tool's output behavior.

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 input schema. The description adds no parameter-specific information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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 'trend analysis on Naver Shopping keywords by gender' with a Korean translation. It specifies the verb 'perform trend analysis' and resource 'Naver Shopping keywords by gender', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_gender' or 'datalab_shopping_keyword_by_age', which would require more specific scope details.

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 multiple sibling tools in the datalab_shopping family (by_age, by_device, category, keywords), there's no indication of when gender-specific keyword analysis is appropriate versus other demographic or categorical analyses. The agent must infer usage from the tool name alone.

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