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

Annotations already declare readOnlyHint, idempotentHint, and closed-world behavior, so the safety profile is covered. The description adds genuinely useful behavioral detail beyond that: that verdicts are "published" (a scoping/curation constraint), and that cost is measured per *successful* task with retries and failures counted — a non-obvious accounting rule an agent could not infer from structured fields.

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 decision question ("Which LLM is the cheapest that still passes?") followed by a compact enumeration of returned fields in one sentence. No filler, though the cost-accounting parenthetical is dense enough to slow the first read.

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, the description carries the burden of describing return values and does so field-by-field, and annotations cover the safety profile of this read-only, idempotent call. The only remaining gap is routing guidance to the get_verdict sibling for single-task lookups.

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% and the single parameter is fully documented with examples (classification, extraction, tool-call). The description restates it as "Optional filter by task type," adding only that omission means all types — consistent with the schema rather than additive to it, so baseline 3 applies.

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?

"Lists every published Clearbar verdict" gives a concrete verb and resource, and the description enumerates exactly what each row contains (task, pass bar, cheapest passing model, cost per successful task, verification page). The plural/"every" framing implicitly opposes it to the single-item sibling get_verdict, but that sibling is never named, so the separation is inferred rather than stated.

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 only usage signal is "Optional filter by task type," which implies the browse-all use case. There is no explicit guidance on when to call this versus get_verdict for a specific task, and no stated preconditions or exclusions.

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