product_details

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses a data-quality trait — that fresh meat and produce usually carry no ingredients or nutrition — which prevents the agent from treating empty fields as an error. However, it says nothing about auth requirements, error behavior on unknown skus, or rate limits.

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?

Two sentences, front-loaded with the verb+resource and the sku source, followed by the return-fields list and a caveat. Every clause carries information — the field enumeration is justified because there is no output schema — though the middle sentence is dense.

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?

In the absence of both an output schema and annotations, the description compensates by enumerating the return payload and flagging the common case of missing ingredients/nutrition data. Auth, error semantics, and sku format remain unstated, which is acceptable for a read-only detail lookup but leaves small gaps.

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 0% and the single sku parameter is undocumented in the schema, so the description must compensate; it does by stating the parameter is a sku and where to obtain it (search_products). It does not specify format (e.g., plain string, numeric pattern) or case sensitivity, which would be the last piece of value-add.

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 gives a specific retrieval verb and resource ("full detail for one product by sku") and distinguishes itself from the sibling search_products by naming it as the origin of the sku. It also enumerates the exact payload (price, ingredients/allergens, nutrition, country attributes, image URL), so the agent knows what it gets versus a search result.

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?

"(from search_products)" establishes the expected workflow and prerequisite: the sku comes from a prior search, not from free invention. It lacks an explicit when-not-to-use statement (e.g., versus price_check for a lightweight lookup), but the context and the data caveat make intended usage clear.

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