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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful operational detail: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also discloses the return fields (ranked list with score, confidence, signal density), which is valuable beyond the 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?

Three sentences, each earning its place: the first defines the action, the second explains mechanism and output, the third gives a concrete use case and quotes an example. Front-loaded with the main verb and no filler or repetition.

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?

The description covers the process (probes with ai_visibility_check, ranks) and return value composition (ranked list with score, confidence, signal density), which is essential since there is no output schema. It does not mention ordering direction (e.g., descending by score), error cases, or model selection behavior, but those are partially covered by schema annotations. Overall, it is sufficient for an agent to invoke and interpret results.

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 baseline is 3. The description does not add parameter-specific meaning beyond the schema; it repeats 'entities' concept ('your brand + N competitors') but does not clarify parameter syntax, formats, or edge cases beyond what property descriptions already provide. The schema itself is thorough, so no extra credit needed.

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 opens with a specific verb+resource: 'Compare AI visibility across multiple entities side-by-side.' It clearly distinguishes itself from siblings like ai_visibility_check (single-entity probe) and compare_entities (generic comparison) by focusing on AI visibility and naming the underlying probe tool. The competitive audit example further anchors 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 states a clear use case: 'Useful for competitive AI-marketing audits' with a concrete question ('does Claude know about us as well as our competitors?'). It mentions probing with ai_visibility_check, implying the single-entity alternative, but does not explicitly say 'use this instead of ai_visibility_check when comparing multiple entities' or provide exclusions. Still, the context is clear enough for an agent to choose appropriately.

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