datalab_shopping_by_device

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

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only says 'trend analysis' without revealing what the output looks like, whether it is read-only, or any limitations. The agent is left blind to the actual behavior beyond a vague operation.

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, front-loaded sentence that efficiently communicates the tool's action and scope. There is no filler, and the parenthetical Korean translation is helpful for multilingual users without adding bloat.

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 absence of an output schema and annotations, the description must explain what the trend analysis returns and how to interpret it, but it does not. The tool has 5 required parameters, and the minimal description is insufficient for an agent to fully anticipate the response.

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 input schema provides descriptions for all 5 parameters, so baseline is 3. The description adds no extra meaning beyond the schema, and it does not explain parameter relationships or constraints beyond what is already documented.

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 a trend analysis on Naver Shopping broken down by device. The verb 'Perform' and scope 'by device' make it distinct from sibling tools like datalab_shopping_by_gender or datalab_shopping_by_age.

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 phrase 'by device' subtly implies use when device-specific trend analysis is needed, but the description gives no explicit when-to-use or when-not-to-use guidance and does not differentiate from keyword-level device tools such as datalab_shopping_keyword_by_device.

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