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

Description adds value beyond annotations by explaining the probing process, ranking, and that the first entity is treated as subject. Annotations already indicate readOnlyHint, idempotentHint, etc., and description does not contradict them.

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 with no wasted words: first states action, second explains process, third provides use case. Front-loaded and efficient.

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?

Without output schema, description adequately explains output (ranked list with score, confidence, signal density). It covers input, process, and output. Could mention error handling or idempotency, but overall sufficient.

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 covers all parameters (100% coverage), but description adds meaning: entities are probed with ai_visibility_check, first entry is subject for narrative, context is shared across probes. This enriches understanding beyond 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 it compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and returns a ranked list with metrics. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities by specifying the competitive audit use case.

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 says 'Useful for competitive AI-marketing audits' and gives an example question, implying when to use it. It does not explicitly state when not to use or name alternatives, but the context of sibling tools makes the purpose clear.

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