datalab_shopping_keyword_by_device

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states it performs 'trend analysis' but doesn't describe what the analysis returns (e.g., time-series data, metrics), whether it's read-only or has side effects, rate limits, authentication needs, or error conditions. For a tool with 6 required parameters and no annotations, this leaves significant 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?

The description is appropriately concise with two parallel sentences (English and Korean). Both sentences directly state the tool's purpose without unnecessary elaboration. However, the Korean translation adds redundancy rather than new information, slightly reducing efficiency.

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 tool with 6 required parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the trend analysis returns, how results are structured, or provide context about the Naver Shopping platform. With rich parameter requirements but poor behavioral disclosure, the description leaves too many gaps for effective tool use.

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 6 parameters with descriptions and enums. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how keyword interacts with device) or provide usage examples. With complete schema coverage, baseline 3 is appropriate.

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

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_device' (which might analyze shopping trends by device without keyword focus) or 'datalab_shopping_keyword_by_age' (which does keyword analysis by age instead of device). There's no context about appropriate use cases or exclusions.

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