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

The description adds operational detail beyond the annotations: it states it probes each entity with ai_visibility_check, ranks results, and treats the first entity as the 'subject' for narrative. The annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the added behavior around how the tool orchestrates sub-probes is valuable.

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, front-loaded with the core purpose, then a use-case, then a return-value summary. No filler or redundant restatement of the schema; every sentence adds value.

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 specifies the return: 'ranked list with score, confidence, signal density per entity'. It also explains the internal call to ai_visibility_check, which clarifies behavior and dependencies. Given the tool's moderate complexity, this is 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%, so baseline is 3. The description adds nuance by clarifying the first entity is treated as the 'subject' and the rest are competitors—this is beyond the schema, which only states 'First entry treated as the "subject" for narrative; rest are competitors' (actually appears in both, but the description additionally gives the default for 'models' via 'Omit for just workers-ai'). That enriches 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 it 'Compare AI visibility across multiple entities side-by-side', with specific actions: probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. This distinguishes it from the sibling ai_visibility_check (which likely handles single entities) and compare_entities by focusing on AI visibility specifically.

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 identifies the use case: 'Useful for competitive AI-marketing audits' with an example query. It does not explicitly mention exclusions or alternatives, but the context is clear enough that an agent would know when to reach for it over simpler single-entity tools.

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