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

Annotations already provide strong behavioral cues (readOnlyHint, idempotentHint, destructiveHint=false), so the description's additional disclosure of return format (ranked list with score, confidence, signal density) adds moderate value. There is no contradiction and the description does not introduce hidden side effects.

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: first states the action and output, second provides usage context and an example. Every word is functional, with no redundancy or filler. Ideal conciseness for an agent.

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?

The description covers the return format (ranked list with score, confidence, signal density) and input constraints (2-8 entities), which is sufficient for a read-only tool with rich annotations. It does not detail error handling, but this is not critical given the annotations and clear usage.

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% (all 4 parameters documented), but the description adds value by explaining that the first entity in the array is treated as the 'subject' for narrative, and that the models parameter defaults to workers-ai if omitted. This goes beyond the schema descriptions.

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, ranks them by score, and identifies most/least recognized. It uses a specific verb ('Compare') and resource ('AI visibility across entities'), distinguishing it from siblings 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 explicitly frames the tool for competitive AI-marketing audits and gives an example question ('does Claude know about us as well as our competitors?'). This provides clear context for when to use it, but does not explicitly exclude alternatives or state when not to use it, leaving some ambiguity with sibling compare_entities.

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