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

The description fully discloses behavior beyond annotations: it probes each entity with ai_visibility_check, ranks by score, identifies most/least recognized, and returns a ranked list with score, confidence, signal density. It also mentions the optional Anthropic API key for certain models. Annotations already note read-only and idempotent, and the description adds valuable workflow details without contradiction.

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 core purpose, then process, then use case. Every sentence adds value, no wasted words. Structure is logical and easy to parse.

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 the tool's complexity (orchestrating multiple probes, optional models/API key, entity constraints) and the lack of output schema, the description covers everything needed: purpose, process, input constraints (2-8 entities), authentication, and return fields (score, confidence, signal density). No 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?

Schema coverage is 100%, so baseline is 3. The description adds meaningful context: the first entity is treated as the 'subject' for narrative, and the tool accepts 2-8 entities. This goes beyond the schema descriptions, which are more generic. Additional details like the default model and API key dependency are already in the schema, so not redundant.

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 purpose: to compare AI visibility across multiple entities side-by-side, using ai_visibility_check and ranking results. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (other dimensions) by specifying the side-by-side comparison with ranking.

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 a clear use case (competitive AI-marketing audits) with an example question. It implies when to use it (for multiple entities) but does not explicitly mention when not to use it or name alternatives. The sibling list and context make the guidance clear, but explicit exclusions would elevate it further.

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