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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds valuable behavioral details beyond annotations: it reveals the tool internally probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. This is richer than the typical annotation baseline and helps the agent anticipate side effects like sub-tool invocations.

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, front-loaded with the primary action, and includes a concrete usage example in quotes. Every sentence contributes information: what it does, how it works (sub-probe and ranking), when to use it, and what it returns. No 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?

The tool has 4 parameters, 1 required, and no output schema. The description covers the core function, return format (ranked list with score, confidence, signal density), and the sub-tool dependency (ai_visibility_check). It also provides a use-case context. Missing are details about rate limits or cost implications of the sub-probes, and it doesn't mention required entity count (2-8) but that is in the schema. Overall, it is sufficiently complete for an agent to use effectively.

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%, so parameter semantics are already fully documented. The description adds little beyond the schema: it mentions 'your brand + N competitors' which loosely aligns with the entities parameter, but the schema already specifies the first entry as the 'subject.' Since the schema carries the parameter documentation burden, the description adds no significant new meaning, warranting the baseline score of 3.

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 opens with 'Compare AI visibility across multiple entities side-by-side,' which clearly states the specific verb (compare), resource (AI visibility), and scope (multiple entities). It also explicitly distinguishes itself from the sibling tool ai_visibility_check by noting it probes each entity and ranks scores, making the purpose unambiguous.

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 provides an explicit use case: 'Useful for competitive AI-marketing audits' with a concrete example ("does Claude know about us as well as our competitors?"). It implies multi-entity comparison but does not explicitly mention alternatives like compare_entities or exclude single-entity checks, so while context is clear, explicit when-not guidance is missing.

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