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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint as false. The description adds behavioral context: it probes each entity with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, and signal density. This goes beyond annotations, but could be more explicit about intermediate calls or potential side effects (none expected). 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 main sentences plus a parenthetical example. It is front-loaded with the primary action, then provides a concrete use-case example. No redundant or extraneous text; every sentence serves a clear purpose.

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 the tool complexity (multi-entity comparison, no output schema), the description adequately explains the process (probes each entity, ranks, returns metrics). It covers what the tool does, how it works, and what is returned. However, it could specify entity count limits (already in schema) or potential rate limits. Overall sufficient for an AI agent to understand and invoke correctly.

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 description coverage is 100% (all four parameters have descriptions). The description adds value by explicitly stating that the first entity is treated as the 'subject' for narrative, which is not in the schema. This enhances understanding of parameter semantics beyond the schema alone.

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 with ai_visibility_check, ranks by score, and surfaces most/least recognized. It uses specific verbs like 'compare', 'probes', 'ranks', and 'surfaces', and distinguishes from the sibling ai_visibility_check by being a multi-entity comparison. The example question solidifies its purpose.

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 mentions 'useful for competitive AI-marketing audits' and gives a concrete example question. It implies usage for comparing multiple entities, which distinguishes it from single-entity tools like ai_visibility_check. However, it does not explicitly state when not to use it or list alternative tools, but the context is clear enough for an AI agent.

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