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

With no annotations, the description still discloses meaningful behavior: data source (schema.org JSON-LD or Shopify JSON), returned fields, and the distinction between cached observed offers and a live uncached Shopify Catalog search. It stops short of stating read-only status, permissions, 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?

The description is compact and front-loads the core resource and return fields before covering matching and optional behavior. It is dense but not padded, though the semicolon-heavy structure could be easier to scan.

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 9-parameter tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It covers the main matching fields and live/zip behavior but omits several filter parameters and does not explain defaults or result limits.

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 0%, and the description only explains five of nine parameters: gtin, mpn, q, zip, and live. Important filters such as domain, in_stock, max_price, and limit are left undocumented in both the schema and the description.

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 that the tool finds product-level offers observed on store pages, including the data source and returned fields. However, it does not explicitly differentiate itself from siblings such as get_landed_price, find_alternatives, or search_merchants.

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 implied usage guidance by explaining matching via gtin, mpn, or q, and notes that zip and live alter behavior. But it does not specify when to prefer this tool over related siblings or when not to use it.

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