visual_assert

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

With no annotations, the description carries the full burden and does add real value: the 64-question batching limit and the crucial 'answer=null means uncertain, not confirmation' semantics. However it omits whether the call captures a screenshot, which target is asserted against in browser vs desktop mode, and any cost/latency or side-effect profile.

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 dense sentences, front-loaded with the batching capability and the null-semantics warning. Minor deduction: 'typed_verified' is referenced without definition and is not one of this tool's parameters, which may momentarily confuse an agent.

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-value explanation is not owed, and the null semantics cover the most important part of the response. Still, an agent does not learn the difference between browser and desktop mode or what visual source is being asserted against, which matters for a vision tool in a 14-sibling browser/desktop toolbox.

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 0%, so the description must compensate and largely does not. It clarifies the `questions` array's cap (64) and element type, but the `mode` enum (browser/desktop) is never mentioned, leaving half the parameters uninterpretable from either source.

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 verb (evaluate) and resource (visual questions) are explicit, and the batching cap of 64 in one model call is a concrete distinguishing trait. It does not state that the questions are answered against a captured screen, so an agent must infer the screenshot/vision mechanism from the name, but the intent is unambiguous and separable from siblings.

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 names preferred alternatives ('Prefer DOM field labels and typed_verified for typing') and an explicit when-not ('avoid repeated visual assertions for an already verified action'). What is missing is a positive trigger — the description never says 'use this when DOM inspection is unavailable/inconclusive'.

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