datalab_shopping_category

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

With no annotations, the description carries the full behavioral burden. The word 'Analyze' implies a read-only trend query and the find_category prerequisite is useful, but it does not disclose return behavior, data source limitations, or any constraints beyond the category-code prerequisite.

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 is front-loaded and the prerequisite follows immediately. The description is not bloated, though the final Korean parenthetical largely duplicates the earlier English instructions and could be trimmed.

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 schema fully documents the four required parameters, and the description supplies the key workflow prerequisite and usage examples. However, with no annotations and no output schema, it leaves some gaps around expected result shape and analytical constraints.

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 baseline is 3. The description adds meaningful parameter guidance by explaining that category values require codes from find_category, and the example shows how category names are compared over a time range.

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 a specific verb and resource: analyze shopping category trends over time, with concrete business cases and an example. It is clear what the tool does, but it does not explicitly distinguish itself from sibling tools such as datalab_shopping_by_device or datalab_shopping_keywords beyond the category 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?

It gives a clear prerequisite by saying to use find_category first for category codes, and the STEP 2 framing implies a workflow position. Business cases and an example provide context, but there is no explicit guidance on when not to use this tool versus the device/gender/age or keyword trend siblings.

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