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

The description adds rich behavioral context beyond the annotations: it explains that the tool 'probes each entity' with ai_visibility_check, 'ranks by score', and returns a 'ranked list with score, confidence, signal density per entity.' It also discloses the conditional _apiKey requirement and the special treatment of the first entity ('First entry treated as the "subject" for narrative'). These details are not present in the annotations and significantly enhance transparency.

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: the first states the primary action and process, the second provides the use case and expected return. It is concise, front-loaded with the main verb, and every clause adds value (method, ranking behavior, use case, output format). No filler or redundancy.

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 moderate complexity (4 parameters, 1 required), the description covers all critical aspects: what it does, how it works (via ai_visibility_check), why to use it (competitive audits), what output to expect (ranked list with score/confidence/density), and parameter nuances. The absence of an output schema is compensated by the explicit mention of the return fields. It is complete for an agent to select and invoke correctly.

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?

While the schema describes each parameter at 100% coverage, the description adds crucial semantic nuances: it states that models is optional and default to workers-ai, that _apiKey is required only for 'anthropic' in models, and that entities has an order-dependent meaning ('First entry treated as the "subject" for narrative'). These elaborated interpretations go beyond the schema's basic descriptions, clarifying parameter relationships and defaults.

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: 'Compare AI visibility across multiple entities side-by-side.' It uses a specific verb ('Compare'), names the resource ('AI visibility'), and distinguishes itself from the sibling tool ai_visibility_check by emphasizing the multi-entity comparison aspect. The phrase 'surfaces which is most/least recognized' further specifies the output and scope.

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 explicit guidance on when to use the tool: 'Useful for competitive AI-marketing audits' and includes a concrete example ('does Claude know about us as well as our competitors?'). It also names the underlying tool (ai_visibility_check) for individual probes, implicitly indicating the alternative for single-entity checks. This is clear context with an implicit exclusion for non-comparative use.

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