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

With no annotations, the description carries the disclosure burden and does add value: it explains the two-round revise-with-visibility behavior and the $0.03 USDC price, which is real context beyond the schema. It does not state latency, how the verdict is delivered, or what happens on partial failure, so it is helpful but not exhaustive.

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 dense sentences, front-loaded with the mechanism before the usage triggers and price. Nothing is padded, though the metaphor-laden phrasing costs a little precision that a plainer sentence could have delivered.

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 paid, multi-model inference tool with no output schema and no annotations, the description covers the mechanism, the cost, and the usage context. Return shape is only implied ('the verdict', low confidence, dissent), which is adequate but leaves the agent guessing about the exact response structure.

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 100%, including the decision-question framing for `q` and payment semantics for `x_payment`/`free_trial`, so the baseline is 3. The description adds no parameter-level detail beyond what the schema already says.

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?

States a concrete mechanism: a 4-model panel over two rounds where each seat sees the others and revises, producing a 'verdict'. It positions itself relative to the `council` sibling ('the same panel', implying a deeper variant), but never distinguishes itself from `council_grounded`, so an agent has to infer which of the two deepened tiers fits.

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?

Gives explicit triggers: use when being wrong is expensive, or after a `council` call returned low confidence or real dissent. That names an alternative and the condition that selects this tool, which is strong routing guidance. The `council_grounded` alternative is left unaddressed, so the selection guidance is clear but incomplete.

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