datalab_shopping_keywords

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

No annotations are provided, so the description carries full burden. It mentions 'trend analysis' which implies a read-only operation, but doesn't disclose any behavioral traits like authentication requirements, rate limits, data freshness, or what the analysis output contains. For a data analysis tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 appropriately concise with a single English sentence and a Korean translation. It's front-loaded with the core purpose. However, the Korean translation adds redundancy without additional information, slightly reducing efficiency.

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 no annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain what the trend analysis returns (e.g., metrics, time series data), how it differs from other shopping keyword tools, or any prerequisites. For a data analysis tool with rich parameter requirements, more context is needed to use it effectively.

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 5 parameters with descriptions. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'keywords' generally but doesn't explain the keyword array structure or how category codes work. Baseline 3 is appropriate when 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 keywords' with a Korean translation for clarity. It specifies the action ('trend analysis'), resource ('Naver Shopping keywords'), and domain context. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_keyword_by_age' or 'datalab_shopping_category', which appear to be related keyword analysis tools with different breakdowns.

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 provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'datalab_shopping_by_age', 'datalab_shopping_keyword_by_gender', and 'search_shop', there's no indication of whether this is the primary keyword analysis tool or how it differs from others that might also analyze shopping keywords with demographic filters.

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