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

Annotations already declare readOnly/openWorld/idempotent, lowering the bar. The description adds valuable behavioral context: it reveals the tool internally probes each entity with ai_visibility_check (multiple sub-calls), ranks by score, and returns confidence and signal density fields. This is consistent with annotations; no contradiction.

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, front-loaded with the core action, then method, then use case. No wasted words; the example quote adds practical intuition without padding.

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?

Despite no output schema, the description specifies the return format (ranked list with score, confidence, signal density). It covers the multi-probe behavior, use case, and key constraints via schema. The annotations cover safety, making this a complete description for a moderately complex tool.

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% with detailed per-param descriptions (models supported values, _apiKey usage, context disambiguation, entities 2-8 with first as subject). The description adds no further parameter-specific nuance beyond reinforcing 'brand + N competitors' and the output's signal density, which is return-output info; baseline 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 a specific verb ('Compare') and resource ('AI visibility across multiple entities'), immediately distinguishing it from single-entity sibling ai_visibility_check and generic compare_entities. It further specifies ranking, competitive audit use case, and returning per-entity scores, making the tool's unique role clear.

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 gives a concrete use case ('competitive AI-marketing audits') and clarifies the multi-entity context ('your brand + N competitors'). It references ai_visibility_check as the underlying probe, implying it is the single-entity alternative, but does not explicitly state when-not-to-use conditions (e.g., 'for one entity, use ai_visibility_check instead').

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