get_product

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

Annotations already establish readOnly, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond those hints: it is a 'live' card, delivery details are city-dependent per variant, promo codes are provided 'for one variant', and the data includes the site's own discount breakdown. This gives the agent a realistic picture of scope without contradicting the annotations.

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 front-loaded with the core purpose and then organized into a clear variant-by-variant breakdown. It is fairly long, but each sentence contributes substantive detail about what the tool returns; minor redundancy exists between the opening summary and the later enumeration.

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 a rich output schema, fully described parameters, and safety annotations, the description supplies enough high-level context for an agent to select and invoke the tool correctly. It explains the product-card scope, the variant and city dimensions, and the promo/delivery inclusions without needing to restate structured schema content.

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%, so the baseline is 3, but the description adds meaning by mapping parameters to output semantics: 'per variant' corresponds to sku_id/include_delivery, and the 'one variant' promo-codes note clarifies include_promos. It also introduces the `skus` framing that helps an agent understand how variant-level results are organized.

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 and resource: 'Full live card of one product', and then enumerates what that card contains (variants, prices, club-card split, delivery, promos, rating). This clearly distinguishes get_product from siblings like search_products, get_reviews, get_store_availability, and compare_products by its single-product, full-card 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?

Usage is implied rather than stated: the description says what the tool returns, so an agent can infer it is for retrieving comprehensive data about one product. However, it never explicitly says when to choose this over a sibling tool or when not to use it, leaving routing to inference.

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