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

Annotations already cover read-only/idempotent/no-destructive, so the bar is lower, and the description adds genuinely useful semantics the annotations cannot: which accuracy_state values mean 'no value, never zero', that inconclusive/not_covered are not answer errors, and that cited sources accompany a contradiction rather than cause it. It stops short of describing pagination or response shape.

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 resource definition, which is good, but the back half is a dense cluster of caveats packed into long sentences with no visual separation, making the interpretation rules harder to scan than they need to be.

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 10-parameter, no-output-schema tool this covers the important semantic pitfalls around accuracy_state and source attribution, but leaving nearly all input parameters unexplained is a material gap given the low schema coverage.

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 10 parameters and only 10% schema description coverage, the description must carry parameter meaning, but it explains almost none of view, limit, topic, cohort, engine, verdict, cursor, audit_id, or market_id. The accuracy_state values it discusses are output states, not input parameters, and only 'latest or a chosen run' loosely hints at run selection.

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 specific verb+resource: factual claims made by AI answers in a run, checked against the brand's confirmed facts, with accuracy and coverage. This is a distinct dataset from siblings like read_perception or read_visibility_results, so an agent can differentiate it, though the description never names an alternative explicitly.

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 real interpretive guidance ('Always state the coverage and quote the fact', 'inconclusive and not_covered are not errors') and implies choosing the latest vs a specific run, but never says when to prefer this tool over read_perception, read_query_evidence, or read_visibility_results.

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