check_ai_shopping_readiness

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

Annotations already declare readOnlyHint=true and openWorldHint=true, and the description is consistent with both (a read-only grading of a public URL). It adds genuinely useful context beyond annotations: the return shape is a 0-100 score per area plus top issues with fixes, which matters because there is no output schema. No mention of runtime, rate limits, or failure modes for unreachable pages.

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, both front-loaded: the first states what is graded and against which consumers, the second states the return shape. The long signal list is dense but every item is a real grading criterion, not filler.

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 one-parameter, no-output-schema tool, the description covers the input scope, the evaluation criteria, and the return format, which is enough to call it correctly. The only gap is that it never states prerequisites or behavior for pages that fail to load or block crawlers.

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?

With a single parameter and 100% schema description coverage (including a concrete URL example), the schema already carries the semantics. The description only reinforces the 'one product page' scope and does not add format or constraint details beyond it, so the baseline 3 applies.

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?

Specific verb ('grade') plus a precisely scoped resource ('one product page'), with the graded signals enumerated so the agent knows exactly what the tool evaluates. The 'one product page' scope implicitly separates it from the store-level audit_store sibling, but no sibling is named explicitly.

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 framing ('signals AI shopping tools use') makes the use case inferable, but there is no explicit when-to-use statement, no when-not, and none of the siblings (audit_store, check_shopify_checkout, check_merchant_center) are referenced as alternatives. The agent must infer that this is the per-page option versus the store-level audit.

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