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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds real behavioral context beyond annotations: per-call cost ($0.06 USDC x402, tiered model pricing) and hard input caps (4 images, 64 questions, audio/video only with model=omni). It does not detail failure modes or latency, keeping it at 4 rather than 5.

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 core mechanism before pricing, and every sentence carries information (inputs, caps, use cases, cost). It is dense but not padded; the pricing tiers could arguably be trimmed, which keeps it from a 5.

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?

For a 6-parameter, nested-object tool with no output schema, the description covers inputs, limits, model selection, use cases and the return shape ('a probability for every answer') plus cost. Error behavior and precise size-based pricing remain unspecified, but the essentials for correct invocation are present.

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?

With only 50% schema coverage, the description meaningfully compensates: it explains that `state` accepts text or JSON, that images are capped at 4, that audio/video require model=omni, and that questions are typed (yes/no, choice, score). This adds constraints and format meaning not present in the schema descriptions.

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 states a precise mechanism: send a state (text/JSON/images/audio/video) plus up to 64 typed questions and receive a probability per answer, with named use cases (routing, triage, approvals, rubric scoring). That is a specific verb+resource and clearly more than a tautology. It stops short of naming a differentiating sibling (`classify`, `grade`, `ask`), so it does not fully earn a 5.

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 gives concrete usage context – 'Built for routing, triage, approvals and rubric scoring' – which tells an agent when this tool fits. There is no explicit 'when not to use' clause or named alternative (e.g. `classify` for simple labels), so it does not reach the exclusion-level guidance of a 5.

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