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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. Description adds that it probes each entity via ai_visibility_check (internal detail), ranks by score, and returns a ranked list with score, confidence, and signal density. No annotation contradiction. Adds moderate behavioral context beyond the safety profile.

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 well-crafted sentences plus an illustrative question. Every part earns its place: purpose, method, use case, and output format. No fluff, efficiently front-loaded.

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?

No output schema, but description explains return format: ranked list with score, confidence, signal density per entity. For a multi-entity comparison tool with rich annotations, this is fairly complete. Minor omission: could mention ordering direction (descending by score).

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%, so parameters are documented. Description adds extra meaning: first entity is treated as 'subject' for narrative, and 'models' parameter explains supported options (workers-ai default, anthropic requires key). This supplements the schema meaningfully.

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?

Description clearly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. The example question and mention of competitive AI-marketing audits make the purpose highly specific and distinguishable from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison).

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?

Description explicitly states the tool is 'useful for competitive AI-marketing audits' and provides a concrete example question, giving clear context for when to use. It implies this tool is for multi-entity comparison rather than single-entity checks, but does not explicitly name alternatives or conditions to avoid. Still, context is strong.

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