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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it calls ai_visibility_check per entity, ranks by score, and returns a ranked list with score, confidence, signal density. This provides useful behavioral context beyond the annotations without contradicting 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, front-loaded with the primary action, and every sentence contributes meaning. No unnecessary words or repetition, achieving high efficiency.

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?

Despite no output schema, the description explains the return format (ranked list with score, confidence, signal density). It covers all necessary aspects: purpose, prerequisites (entity list, optional models, context, API key), and expected output, making it complete for agent use.

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%, and the description adds valuable context: first entity is treated as 'subject' for narrative, models parameter explains default and Anthropic API key requirement, and context parameter clarifies it disambiguates common names. This significantly enhances parameter understanding.

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 compares AI visibility across multiple entities side-by-side, ranks by score, and surfaces most/least recognized. It uses specific verbs (compare, probes, ranks) and distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic 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?

The description provides an example use case (competitive AI-marketing audits) and an example question. It implicitly contrasts with ai_visibility_check by noting it probes each entity with that tool. However, it lacks explicit 'when not to use' or direct alternative naming, though sibling context fills this gap.

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