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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds value by explaining the sub-probing mechanism (calls ai_visibility_check per entity), the ranking process, and the output format (score, confidence, signal density). It also notes that the first entity is treated as the 'subject' for narrative, a behavioral detail not in annotations.

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?

Two sentences plus a parenthetical example. The description is front-loaded with the main action ('Compare AI visibility across multiple entities side-by-side') and every sentence adds distinct information. No fluff, perfectly sized for a tool with 4 parameters.

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 appropriately explains the return value (ranked list with score, confidence, signal density). It covers the process (probe each, rank), the subject treatment, and the use case. Minor gaps: does not mention maximum entities or error behavior, but overall sufficient for typical 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 coverage is 100%, so the baseline is 3. The description adds some nuance beyond the schema: it mentions that the first entity is treated as the subject and that omitting the models parameter defaults to workers-ai. However, these are minor improvements; the schema already describes parameters adequately.

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 uses specific verbs ('Compare', 'Probes', 'ranks') and clearly states the resource (AI visibility across multiple entities) and outcome (ranked list with most/least recognized). It distinguishes from sibling tool 'ai_visibility_check' by describing a multi-entity comparison that uses that tool internally, making the purpose unambiguous.

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 clear context ('competitive AI-marketing audits') and an example question. It implies when to use (comparative analysis) but does not explicitly state when not to use or list alternatives (e.g., for a single entity, use ai_visibility_check). Still, the context is strong enough for an AI agent to infer appropriate use.

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