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

Beyond the readOnlyHint/openWorldHint annotations, it discloses the extraction method (JSON-LD, Microdata, OpenGraph, no browser), the null-on-missing-facts convention, the scope limitation ('checkout support is not assumed'), and a per-result cost with payment rail. This is exactly the operational context annotations do not carry.

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?

Four short sentences, each earning its place: purpose, method, data-quality semantics, cost. Purpose is front-loaded and there is no padding.

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?

With an output schema present, return shape need not be described. The description still supplies method, null behavior, scope boundary, and pricing, leaving nothing an agent needs to invoke it correctly.

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% for the single product_url parameter. The description adds the 'public' qualifier and the no-checkout scope, but leaves format/URI and maxLength entirely to the schema, so it only partially compensates.

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?

States a specific verb (extract) and resource (product price, stock, variants, ratings, shipping) scoped to a public product URL. It is clearly distinguishable from sibling url_to_clean_markdown, which returns markdown rather than structured product facts.

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?

Opens with 'Use to extract...' and pins the input domain to a public product URL, which is clear context for selection. It does not explicitly name a when-not condition or a specific alternative sibling, so it falls short of a 5.

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