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

Annotations already state readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context: dual-model verification, automatic escalation on disagreement, and the return of a confirmed/rejected decision, confidence score, reasoning, and audit trail. It also notes the 'Pro plan or higher' requirement, which is beyond the annotations.

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?

The description is three sentences, each with distinct value: purpose, mechanism, and outputs/requirements. It is front-loaded with the core question and lacks redundancy or filler.

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?

Despite no output schema, the description lists the return components (decision, confidence, reasoning, audit trail) and discloses plan requirements. For a moderately complex tool with nested objects, it covers purpose, behavior, and outputs well. It doesn't cover error handling or edge cases, but annotations plus schema fill most gaps.

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?

The input schema covers all three parameters with descriptions, so the baseline is 3. The description adds a concrete example for the 'entity' parameter but does not elaborate on 'taxonomy_id' or 'raw_evidence' beyond the schema's definitions. No additional parameter semantics are needed.

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 clearly states the tool's function: checking whether an AI-surfaced brand or entity is a genuine competitor. The verb 'Check whether' plus the specific resource ('brand or entity') and the example ('Banner Life' vs. Enact) distinguish it from sibling tools like scan_visibility, which likely focuses on a brand's own visibility rather than competitor adjudication.

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 description gives a clear usage context: when an AI engine surfaces a brand/entity and you need to verify its competitive status. The example further anchors the scenario. However, it does not explicitly mention alternative tools or when NOT to use this tool, so it stops short of a 5.

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