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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description's burden is lower. It adds valuable behavior context: that it probes each entity, ranks by score, and returns confidence and signal density. It also reveals the internal dependency on ai_visibility_check, which is useful for understanding cost and behavior, though it doesn't discuss rate limits or the impact of the models parameter.

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 sentences: the first states the core capability, the second gives the use case and return value. Every sentence earns its place; there is no fluff 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?

Given the tool's moderate complexity (4 params, no output schema), the description covers the what, why, and expected return (ranked list with score, confidence, signal density). It also explains the internal process (probes with ai_visibility_check and ranks by score). It could be slightly more explicit about how the models parameter changes behavior, but the schema already covers that detail.

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% — every parameter is already documented in the input schema, including the 'first entry treated as subject' nuance for entities. The description adds no new parameter-level semantics beyond what the schema provides, so the baseline of 3 is appropriate.

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 'Compare AI visibility across multiple entities side-by-side,' a specific verb, resource, and scope. It clearly differentiates from the sibling ai_visibility_check by emphasizing multiple entities and side-by-side comparison, and from generic compare_entities by focusing on AI presence.

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 concrete use case: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' It implicitly suggests the alternative of using ai_visibility_check for single-entity probes by naming it as the internal mechanism, though it does not explicitly state when not to use this tool.

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