Verify Storefront Legitimacy

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

Beyond the annotations, the description discloses important behavioral traits: results are point-in-time and should not be cached, the tool never throws and degrades gracefully by returning null signals, and risk outputs are advisory rather than guarantees. This adds substantial behavioral context that annotations alone do not provide.

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?

Although lengthy, the description is well-structured with clear sections (Args, Returns, Recommendation values, Examples, Error Handling) and every section serves a distinct purpose. Core guidance is front-loaded, and the length is justified by the tool's behavioral complexity.

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 no output schema present, the description compensates fully by explaining the return structure, each recommendation value, and the suggested agent behavior for each outcome. It also covers error handling and usage boundaries, making the tool callable correctly without additional external knowledge.

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 schema already fully documents the single parameter with a clear description, including the example and the instruction to strip protocol/path. The description's Arg section essentially repeats this information without adding significant new meaning, so the schema carries the burden and the baseline of 3 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 clearly states a specific verb ('Checks whether') and a specific resource ('a merchant storefront'), with a concrete decision context ('BEFORE an agent commits payment'). It also clarifies what the tool does not do ('does NOT guarantee a storefront is safe'), leaving no ambiguity about its role.

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 provides explicit 'Use when' and 'Don't use when' guidance, including concrete scenarios like submitting payment to a newly found storefront, comparing candidates, and avoiding routine repeat purchases. It also maps return values to specific agent behaviors, so an agent knows exactly when to proceed, pause, or block.

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