datalab_shopping_keyword_by_device

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

No annotations exist, so the description carries the full disclosure burden, yet it says nothing about output shape, data freshness, rate limits, or error behavior. It only adds a vague "perfect for understanding mobile vs desktop shopping behavior" framing, which is marketing rather than behavioral detail.

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 purpose and prerequisite are front-loaded, but the content is then restated verbatim in Korean, and the emoji-prefixed marketing sentence adds no callable information. Roughly half the text is redundant.

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?

A six-parameter, all-required analytics tool with no annotations and no output schema needs the description to explain return content, granularity, and constraints. Instead it explains nothing about results or date-range behavior, so an agent cannot predict 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?

Schema coverage is 100% and already documents all six parameters including enums for device and timeUnit. The description's only added parameter meaning is that category expects a code obtained from find_category, which is useful but thin.

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 keyword performance broken down by device inside shopping categories. This clearly separates it from siblings like datalab_shopping_by_device (no keyword) and datalab_shopping_keyword_by_gender/age (different dimension).

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?

"Use find_category first to get category codes" gives an explicit prerequisite and names the sibling to call first. It does not, however, state when to pick this tool over the by_gender/by_age keyword variants, leaving the dimension choice implicit.

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