Shopify launch watch for e-commerce agents (x402)

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

Annotations already establish readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds genuinely useful behavior beyond that: results are cached up to 60 minutes (so data may be stale) and the target store is fixed and public (so the agent cannot supply or influence it). It does not say what the returned sample contains.

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 short sentences, with the single most decision-relevant fact ("Free.") front-loaded. Nothing repeats the schema or annotations.

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?

Complexity is low and annotations plus the empty schema cover invocation mechanics. However, the tool's entire stated purpose is to let the caller "see the output," and with no output schema the description should sketch what that sample looks like — otherwise the agent cannot judge whether previewing it answers its question.

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 takes zero parameters, which sets the baseline at 4. The description reinforces this by stating the store is fixed, so the agent knows nothing is configurable.

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 conveys that this is a free demo run against one fixed public store so the caller can preview output, which distinguishes it from the paid sibling watch_shopify_launches. But it never names a verb or defines the resource — "the watch" is unexplained jargon, and the title "Free sample" does little to clarify what is actually being sampled beyond a vague preview.

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?

"So you can see the output before paying" implies the usage context: try this before committing to a paid subscription. There is no explicit when-not guidance, no naming of the sibling tool as the alternative, and no statement about limits (e.g., one call, one store, no configuration).

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 cover read-only/idempotent/open-world, and the description adds high-value behavior not in them: the $0.01/store USDC-on-Base charge and, critically, that the first call intentionally returns payment requirements rather than results — an agent would otherwise misread that as a failure. It also bounds the data source to public /products.json.

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?

Front-loaded with the action and the payoff (launch flags), then cost and payment mechanics. The per-store field list is long and runs as a single colon-led sentence, which is dense but each item earns its place by telling the caller which signals come back.

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, so the description carries the return-value burden, and it does: per-store verdict, product count, product fields, and named flags. Combined with the payment path and data-source scope, an agent has everything needed to call this and interpret the first unpaid response.

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 50% and the description compensates by stating the 20-store cap and domain format, plus the semantic meaning of the window through the flag vocabulary (fresh_launch, long_survivor, recently_updated) that since_days drives. It stops short of restating since_days' 1-90 range, but the schema covers that.

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: submit up to 20 store domains and get back a per-store Shopify launch report. The enumerated output fields (product count, newest products, flags, cross-store similar titles) make the tool's identity unmistakable and clearly distinct from the unrelated sibling 'demo'.

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?

Gives clear operating context: 1-20 domains, reads only public /products.json, and an explicit payment protocol (first unpaid call returns payment requirements; pay via _meta['x402/payment'] or the HTTP POST fallback). It doesn't name an alternative tool or state when-not-to-use, but the sibling set gives nothing to disambiguate against.

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