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

The description discloses that the tool probes each entity using the sibling tool ai_visibility_check and returns a ranked list with score, confidence, and signal density. This adds behavioral context beyond the annotations (readOnly, idempotent, etc.), which already indicate safety but do not explain the delegation to another tool or output contents.

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 concise and front-loaded with the core purpose in the first sentence. Each subsequent sentence earns its place by explaining the mechanism, use case, and return format without redundancy or fluff.

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 key aspects for a tool with no output schema: it states what the tool does, how it works (via ai_visibility_check), what it returns (ranked list, score, confidence, signal density), and when to use it. It does not mention edge cases or error behavior, but for the complexity of this tool, the description is sufficiently complete.

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?

The input schema has 100% coverage for all four parameters, including detailed descriptions of entities, models, _apiKey, and context. The description adds only a paraphrase ('your brand + N competitors') that echoes the schema's entity description, providing no new semantic meaning beyond what the schema already contains. Thus, 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 clearly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It names the specific resource (i.e., AI visibility) and the verb 'compare', and distinguishes itself from sibling tools like ai_visibility_check (single-entity) and compare_entities (generic) 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 clear use case ('competitive AI-marketing audits') and an illustrative question ('does Claude know about us as well as our competitors?'). It does not explicitly mention alternatives or when not to use, but the context is well-defined, satisfying the 'clear context, no exclusions' level.

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