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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: it probes each entity with ai_visibility_check, ranks results, and surfaces the most/least recognized. It also mentions returning ranked lists with score, confidence, and signal density. There is no contradiction with 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?

The description is two sentences long with a parenthetical example. It is front-loaded with the core purpose, and every sentence provides unique value. No redundant or filler content.

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 adequately covers the return format (ranked list with score, confidence, signal density). It explains the workflow (probes with ai_visibility_check). However, it does not mention potential errors, rate limits, or the need for an API key when using the 'anthropic' model, though that is in the parameter schema. Overall sufficient for the tool's complexity.

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% with descriptions for all four parameters. The description adds meaning beyond the schema: it notes that the first entity in 'entities' is treated as the 'subject' for narrative, and that 'context' is shared across probes to disambiguate common names. This helps the agent understand parameter relationships.

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, probes each with ai_visibility_check, ranks by score, and identifies most/least recognized. This distinguishes it from sibling ai_visibility_check (single entity) and compare_entities (general comparison). The verb 'compare' and resource 'AI presence' are specific.

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 a clear use case: 'competitive AI-marketing audits' with an example question. It implies the tool is for multi-entity comparison, differentiating from the single-entity sibling. However, it does not explicitly state when not to use it or mention alternatives like ai_visibility_check for single probes.

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