ai_use_statement_required

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

With no annotations, the description carries the full burden, and it does disclose one genuinely important behavior: an empty answer means no cited rule exists and nothing follows from that absence. This prevents the common misreading of null as 'not required'. It stops short of stating read-only status or result shape explicitly, but the dataset-query framing makes the read nature evident.

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 with no filler, and the critical null-semantics caveat is stated up front rather than buried. The opening noun phrase ('Rows on whether...') is slightly awkward as a tool summary but costs little.

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?

An output schema exists, so return structure need not be described, and the tool is simple with one required parameter. However, the missing office-value format is a real gap for actually invoking it, and the description says nothing about coverage across offices or row granularity.

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 single parameter 'office' has 0% schema description coverage and no enum, so the description must compensate. It only obliquely indicates that 'office' means a patent office; it gives no format, naming convention, or accepted values, leaving the agent to guess whether to pass a code, an acronym, or a full name.

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 resource: rows recording whether a given patent office requires an AI-use statement. That is concrete enough to separate it from siblings like rep_required or fee_schedule, but it is phrased as a noun fragment rather than a verb+resource, and it never explicitly contrasts itself with those other rules 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 only implied: the agent can infer it should call this when it needs an office's AI-disclosure rule. The second sentence gives a valuable interpretation rule for empty results, but there is no explicit when-to-use/when-not-to-use guidance and no named alternative among the many sibling rules tools.

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