audit_listing

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

With no annotations, the description carries the full burden and does so well: it discloses that matching is deterministic and rule-pack-driven rather than LLM-based, that findings carry a rule id and rationale and are reproducible, and it explicitly addresses false-positive behavior ('secure' is not flagged as 'cure'). This goes well beyond the 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?

Three sentences, all front-loaded on the core purpose with escalating detail about detection scope and matching strategy. Dense but largely waste-free; the matching example sentence is long yet directly justifies the tool's reliability claim, so it earns its place.

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, and the description compensates by explaining the shape of findings (rule id plus rationale) and the severity model implied by failOn. Enough is present for an agent to call the tool and interpret results, though the exact finding payload structure is left implicit.

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%, so the schema already documents all seven parameters including the platform rule-pack details and aiDisclosure semantics. The description adds the conceptual rule-pack framing but no parameter-specific syntax or edge-case guidance beyond what the schema provides, so the baseline 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 names a specific verb (audit) applied to a specific resource (cross-border e-commerce product listing) and enumerates the exact violation classes detected. It is immediately distinguishable from siblings like search_rules and suggest_rewrite, which serve different phases of the workflow.

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?

It clearly states the timing context ('BEFORE it gets flagged or removed by the platform'), which tells an agent when this tool belongs in a workflow. However, it never names an alternative tool or a when-not-to-use condition, so routing versus list_rule_packs/search_rules is left to inference.

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