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 the full burden of behavioral disclosure. It only states the analysis intent, but fails to disclose return format, read-only nature, limitations, or any effects. This is a significant gap for a tool with no output schema.

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 conveys the core purpose. No wasted words or redundant information.

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?

The tool has 6 parameters and no output schema, yet the description provides no information about return values, example usage, or constraints. The description is too minimal to fully prepare an agent for invocation.

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 67%, with startDate, endDate, keyword, and category described. The description adds no additional parameter semantics beyond the schema, and timeUnit/gender remain unexplained. The coverage is moderate, so a baseline of 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 analyzes Naver shopping keyword click ratios by device (PC vs mobile), using specific category keywords. This verb+resource+scope distinguishes it from sibling tools like datalab_shopping_keyword_by_age and by_gender.

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 implies the tool is used when analyzing device-specific click distribution for shopping keywords, but it does not explicitly state when to use it over alternatives or provide exclusions. The context is clear from the purpose, but no explicit alternatives are mentioned.

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