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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the bar is lower. The description adds meaningful behavioral context by explaining it probes each entity with ai_visibility_check, ranks by score, and returns a structured list with score, confidence, and signal density. It doesn't contradict 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 long, front-loaded with the core purpose, and includes a relevant example. It avoids redundancy with the schema while providing essential behavioral and return-value information. Every sentence earns its place.

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 no output schema, so the description's explanation of the return format (ranked list with score, confidence, signal density) is essential and provided. It also clarifies the relationship with ai_visibility_check and the entity semantics. It doesn't explicitly mention API key requirements for anthropic models, but that is covered in the schema. Overall, it is sufficiently complete for a comparison tool.

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 100% schema description coverage, the baseline is 3. The description adds value by specifying that the first entity is the 'subject' while the rest are competitors, a semantic rule not present in the schema. It also reinforces the role of the models parameter and API key.

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 it 'Compare[s] AI visibility across multiple entities side-by-side' and describes the probing and ranking process. It distinguishes itself from the sibling tool ai_visibility_check by mentioning it probes with that tool, making it specific to multi-entity comparisons.

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?

It gives a concrete use case for competitive AI-marketing audits and an illustrative question. While it doesn't explicitly mention alternatives or when not to use it, the description implies that ai_visibility_check is for single entities, which serves as a clear contrast. A slightly more explicit exclusion would elevate it.

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