Surfaced: Shopify AI Shopping Readiness

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

Annotations already establish the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower. The description adds genuinely useful operational context the annotations do not: results are cached for 30 minutes and the tool is rate limited per client, plus it discloses what data sources are fetched.

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 tight sentences that front-load the core capability before the operational caveats. Every clause carries information — scoring range, inputs, what is returned, and the caching/rate-limit constraints — with no 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?

No output schema exists, but the description still conveys the return shape ('top issues with how to fix them') and the scoring range, and it flags caching that could surprise an agent re-calling after a change. Coverage is good for a two-parameter read tool, with only minor gaps around error behavior for non-Shopify or private URLs.

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 100%, so both parameters (store_url, language enum with default) are fully documented in the schema. The description adds nothing about parameter formatting or the effect of the language option, so the baseline 3 is correct.

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 and resource with scope: scores a public Shopify store 0-100 for AI shopping assistants, and names the exact inputs it inspects (public catalog, robots.txt, policy pages, structured data). This is clearly distinguishable from the sibling explain_issue and get_surfaced_plans tools.

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 by the scoring framing — you'd call it to audit a store — but the description never states when to use it versus the siblings, nor any preconditions (e.g. that the store must be publicly reachable). 'Read-only' is the only routing-ish guidance and that is already in annotations.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds genuine behavioral context — that the return is both an explanation of the issue and remediation guidance — plus concrete example issue ids. It doesn't describe the shape of the response, which slightly limits it given there is no output schema.

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?

One sentence, front-loaded with the core purpose and remediation value; the parenthetical examples earn their place by clarifying the parameter domain.

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 single-parameter read tool with no output schema, the description covers what is returned (meaning plus fix guidance) and where the id comes from, which is sufficient to call it correctly. It could add a bit on response structure, but nothing essential is missing.

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% and the schema already says the id comes from top_issues[].id, so the baseline is 3. The description goes further by enumerating representative issue ids (altText, gtin, jsonld, policy:refund-policy), which conveys the domain of valid values beyond the schema text.

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 ('returns what ... means and how to fix it') plus the resource (a readiness issue id), and explicitly ties it to the sibling check_store_ai_readiness as the source of those ids. An agent can distinguish it from check_store_ai_readiness without opening either schema.

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 description makes the usage context clear: this is a follow-up lookup for an issue id produced by check_store_ai_readiness. It does not state explicit when-not-to-use conditions or name alternatives beyond that, so it stops 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.

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

The annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds only that it returns plan information and an install link, without details about authentication, response shape, or any operational constraints.

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 a single efficient sentence that front-loads the core action and result. The trailing audience context is brief and does not obscure the main purpose.

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 zero-parameter, read-only tool with no output schema, the description sufficiently identifies what is returned: the app plans and install link. The annotations cover the safety behavior, so no critical information is missing for correct invocation.

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?

The tool has zero parameters, so parameter semantics are not applicable in the usual sense. With no parameters to document, the baseline score of 4 is appropriate.

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 verb and resource: it lists Surfaced Shopify app plans and the install link, and it names the plan tiers. It is clear what the tool does, but it does not explicitly distinguish itself from sibling tools such as check_store_ai_readiness or explain_issue.

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 phrase 'for merchants who want every product scanned and safe fixes applied with undo' implies a usage context or target audience. However, the description does not state when to use this tool versus alternatives, nor does it mention exclusions or prerequisites.

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