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

Annotations already cover readOnly, idempotent, and non-destructive. The description adds valuable behavioral context: it probes each entity internally, ranks by score, and returns score/confidence/signal density per entity. This goes beyond annotations and helps set expectations about the multi-step nature and output. No contradictions.

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, each sentence earning its place: the first states purpose, the second explains method and use case, the third describes output. It is front-loaded with the core action and contains no fluff or redundant information.

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?

Since there is no output schema, the description appropriately explains the return format (ranked list with score, confidence, signal density). It also covers the workflow and intended use case. Minor gaps like not mentioning potential cost of multiple model probes or entity constraints beyond the schema are acceptable, but overall it is reasonably complete.

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% for all four parameters, so baseline is 3. The description adds meaning beyond the schema by stating 'First entry treated as the "subject" for narrative; rest are competitors.' This clarifies the role of the first entity, which is not obvious from the parameter description alone.

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 'Compare AI visibility across multiple entities side-by-side', specifying the verb 'compare' and resource 'AI visibility'. It distinguishes from the sibling tool ai_visibility_check by emphasizing the multi-entity side-by-side comparison, and from compare_entities by focusing specifically on AI visibility 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?

It provides a concrete use case ('competitive AI-marketing audits') and an illustrative question, making when to use it clear. It also implies the difference from ai_visibility_check by saying 'Probes each entity with ai_visibility_check', suggesting this is the multi-entity version. However, it does not explicitly state 'when not to use' or name alternatives, though the context is strong.

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