datalab_shopping_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 mentions 'trend analysis' but doesn't specify what that entails (e.g., returns data, charts, insights), whether it's read-only or has side effects, or any constraints like rate limits or authentication needs. This is inadequate for a tool with 5 required parameters.

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 brief and front-loaded with the core purpose. The Korean translation adds redundancy but doesn't significantly detract. It could be more concise by omitting the translation, but it's still efficient with no wasted sentences.

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 5 required parameters, no annotations, and no output schema, the description is incomplete. It lacks behavioral context (e.g., what the analysis returns, any limitations), usage guidance relative to siblings, and doesn't compensate for the absence of structured metadata. This leaves significant gaps for an agent.

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 parameters thoroughly. The description adds no parameter-specific information beyond implying device analysis, which is covered by the 'device' parameter's enum. Baseline 3 is appropriate when the schema does the heavy lifting.

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 by device' with a Korean translation. It specifies the verb ('perform trend analysis'), resource ('Naver Shopping'), and scope ('by device'), but doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_by_gender' beyond the device focus.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_by_gender' for comparison, nor does it specify prerequisites or exclusions. The description only states what it does, not when it's appropriate.

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