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

Annotations already indicate safe read-only, idempotent behavior. The description adds valuable context beyond annotations: it reveals that the tool internally calls ai_visibility_check, ranks results by score, and returns a structured list (score, confidence, signal density). It also notes the first entity is treated as subject, and that the Anthropic model requires an API key. No contradictions with 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?

Description is compact and front-loaded: the first sentence states the core function, the second explains the mechanism, and the third provides an example use case with expected output. Every sentence earns its place, with no fluff.

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 tool with moderate complexity, the description covers the key aspects: what it does, how it does it (probes ai_visibility_check), what it returns (ranked list with specific fields), and a concrete use case. The absence of an output schema is partially mitigated by explicitly naming the return fields. No critical information seems missing.

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%, so parameters are fully documented in the schema. The description adds extra semantic nuance, particularly that the first entity is treated as the 'subject' for narrative, and clarifies the tool's behavior of probing each entity. This goes slightly beyond the schema descriptions, justifying a score above baseline.

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?

Description uses a specific verb ('Compare') and resource ('AI visibility across multiple entities'), explicitly stating it probes each entity with ai_visibility_check, ranks them, and identifies most/least recognized. This clearly distinguishes it from single-entity tools like ai_visibility_check and generic comparison tools like compare_entities.

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 clearly states the use case ('competitive AI-marketing audits') and provides a concrete example. It implicitly contrasts with single-entity checks by mentioning 'your brand + N competitors', but does not explicitly name alternatives or state when to avoid using it. This is a minor gap.

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