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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is covered. The description adds valuable behavioral detail: it internally calls ai_visibility_check per entity, ranks by score, and returns specific metrics. This goes beyond what annotations provide and is consistent with them.

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 two sentences long, with the core action front-loaded in the first sentence and the second covering use case and output. Every word earns its place; 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?

Given 4 parameters, no enums, and no output schema, the description provides enough context: purpose, method, output structure, and a typical query example. Minor gap is not explaining what 'score' or 'confidence' represent conceptually, but the overall tool behavior is well-covered.

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%, with each parameter already fully described (e.g., 'entities' with 'First entry treated as the subject', 'models' with supported values, 'context' for disambiguation). The tool description does not add extra parameter semantics beyond restating that entities are probed and ranked, so baseline 3 applies.

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 uses the specific verb 'Compare' with a clear resource ('AI visibility across multiple entities') and explicitly states the method (probes with ai_visibility_check, ranks by score) and output (ranked list with score/confidence/signal density). It distinguishes itself from the sibling ai_visibility_check by framing itself as a side-by-side multi-entity comparison.

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?

Provides a concrete use case ('competitive AI-marketing audits') and an example question, which clearly implies when to use it. It does not explicitly mention alternatives or exclusions, but the context of probing 'your brand + N competitors' makes it obvious this is the multi-entity version of ai_visibility_check.

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