datalab_shopping_keyword_by_device

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the tool performs a trend analysis, implying a read operation but not specifying return format, whether it is read-only, rate limits, or required authentication. This lack of detail is insufficient for an agent to anticipate behavior or output structure.

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 very concise, with a single English sentence and a redundant Korean translation. It is front-loaded and avoids unnecessary words. However, the Korean translation adds no unique value, so it is not maximally efficient, but it is still appropriately brief.

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 has 6 required parameters, no output schema, and no annotations, the description is incomplete. It does not explain return values, result structure, or how keyword-level trends differ from category-level trends. This lack of context makes it hard for an agent to fully understand the tool's behavior and output, even though the schema clarifies input.

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%, with all six parameters described in the input schema. The description adds no extra parameter semantics beyond what the schema already provides, such as the 'by device' qualifier which is already captured in the device enum. Baseline 3 is appropriate since 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 states the action ('Perform a trend analysis') and the resource ('Naver Shopping keywords by device'), clearly identifying the core function. It does not explicitly distinguish from sibling tools like datalab_shopping_keywords or datalab_shopping_by_device, but the 'by device' qualifier provides some differentiation. The verb is specific and the resource is well defined.

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 offers no guidance on when to use this tool versus alternatives. It does not mention scenarios for which it is appropriate, nor does it contrast with siblings such as datalab_shopping_keyword_by_gender or datalab_shopping_by_device. There is no exclusions or contextual advice, leaving the agent to infer usage from the name alone.

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