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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density. This explains the internal process and output format beyond annotations, 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?

Three sentences: first states purpose, second explains process, third gives use case and output. Front-loaded, no wasted text. 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?

Given no output schema, the description explains return format (ranked list with score, confidence, signal density). For a read-only aggregation tool with good annotations, this is sufficient. Doesn't cover error cases or limits, but overall complete enough for effective 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 covers 100% of parameters with descriptions. The description adds meaning: first entity is treated as subject for narrative, rest as competitors. This goes beyond schema, aiding correct usage. However, it doesn't detail the implications of models or _apiKey beyond what schema says.

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, using ai_visibility_check probes and ranking. This specific verb+resource distinguishes it from the sibling 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?

It explicitly mentions use case: competitive AI-marketing audits, with an example question. It implies when to use (comparing multiple brands) but does not explicitly state when not to use or name alternatives, though ai_visibility_check is a clear alternative for single entity inspection.

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