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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds meaningful behavioral details beyond annotations: it probes each entity with ai_visibility_check, ranks by score, and returns score, confidence, and signal density per entity. This gives the agent a concrete expectation of the tool's internal process and output format, compensating well for the lack of an output schema.

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 three sentences, each earning its place: the first states the primary action, the second explains the method and use case, and the third describes the return value. It is front-loaded with the main purpose and contains zero filler or redundant repetition of the tool name or schema fields.

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?

For a read-only tool with rich annotations and a 100% schema coverage, the description gives enough context: the underlying mechanism, the ranking behavior, and the output fields. It doesn't explain edge cases like the 8-entity limit, but that's already in the schema. The only minor gap is the meaning of 'signal density', but it's reasonably self-explanatory. Overall, the description is complete for the tool's complexity.

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 for parameters is 100%, so the schema already provides descriptions for all four params. The description adds a slight nuance by framing 'entities' as 'your brand + N competitors', which reinforces the first-entity-as-subject note in the schema, but it doesn't add substantial meaning beyond what the schema already provides. Baseline 3 is appropriate here.

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 leads with a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It clearly distinguishes itself from the sibling ai_visibility_check by framing this as a multi-entity comparison that uses ai_visibility_check as the underlying probe. It also specifies the output (ranked list, most/least recognized), making the tool's purpose unmistakable.

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 ('competitive AI-marketing audits') and an explicit example question that shows when this tool is appropriate. It does not explicitly name alternatives or exclusion criteria, but referencing ai_visibility_check as the probe implies this is for multi-entity comparisons, giving the agent enough context to choose it over single-entity checks.

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