get_product_variants

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

With no annotations provided, the description carries the transparency burden. It discloses the output contents (inventory matrix and image URL) and even mentions the underlying Shopline API endpoint. However, it does not address error cases (e.g., invalid product_id) or response structure in detail, though for a read-only query this is a minor gap.

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 concise and well-structured: first sentence states the purpose, second provides usage guidance, third offers an alternative, and the final block gives the API endpoint. Every sentence is essential, and the key information is front-loaded.

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 simple read tool with one parameter and no output schema, the description is complete: it specifies the use case, the required input, the return content, and even the underlying API call. It fully compensates for the lack of annotations and output schema given the tool's low complexity.

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 the product_id parameter already described as coming from get_product_list / get_product_by_sku. The description reinforces this prerequisite, but adds no new semantic detail beyond the schema, so it stays at the baseline.

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 opens with a specific verb '取得' (get) and the resource '特定商品的所有 SKU 變體明細' (all SKU variant details for a specific product), further detailing the size×color inventory matrix and image URL. This clearly distinguishes it from sibling tools like get_product_list and get_product_by_sku by emphasizing the variant matrix scope.

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 explicitly states when to use the tool: when the product_id is already known and a complete variant matrix is needed. It also explicitly directs users to get_product_by_sku / get_product_list first if only SKU or product name is known, providing clear alternatives and exclusions.

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