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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds value beyond these by disclosing the probing mechanism (uses ai_visibility_check), the ranking behavior, and the output fields (score, confidence, signal density). This is useful behavioral context not available in annotations. It doesn't contradict 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?

The description is three sentences long and every sentence contributes: purpose, process, use case, and output. It is front-loaded with the main action and ends with a concrete output summary. No redundant or filler content.

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?

Even though there's no output schema, the description explicitly states what will be returned (ranked list with score, confidence, signal density per entity), covering the most important output aspects. It also explains the underlying dependency on ai_visibility_check. The entity count range is in the schema, and the description covers the tool's role in competitive audits, making it sufficiently complete for an agent to invoke confidently.

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 the schema already documents each parameter thoroughly, including the special meaning of the first entity and optional models/_apiKey/context. The description largely restates the entity semantics ("your brand + N competitors") without adding new parameter-level insight. Thus the baseline 3 is appropriate; no extra compensation 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 clearly states the tool's function with a specific verb ("Compare") and resource ("AI visibility") across multiple entities. It distinguishes itself from the sibling ai_visibility_check by explicitly mentioning side-by-side comparison and ranking, and from compare_entities by focusing on AI presence. The phrase "Probes each entity ... with ai_visibility_check, ranks by score" further clarifies the unique behavior.

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 clear use case: "Useful for competitive AI-marketing audits" and provides an illustrative example question. It implies when to use this tool over single-entity alternatives, though it doesn't explicitly name when-not-to-use conditions or alternatives. The context is strong enough for an agent to select this tool appropriately.

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