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

Annotations already declare read-only/open-world/idempotent, and the description adds valuable behavioral context: it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, and signal density. No contradiction with annotations; the methodology is transparent.

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 purpose, then how it works and a use case. Every sentence adds value with no fluff or redundancy.

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 tool with no output schema, the description covers what, when, how, and the return format (ranked list with score/confidence/signal density). Minor gaps exist (e.g., ranking methodology details) but are not essential for selection.

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 property descriptions, so the baseline is 3. The description restates the 'entities' concept but adds no new parameter-level semantics beyond what the input schema already provides (e.g., first entity as subject).

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 states a specific verb ('Compare') and resource ('AI visibility across multiple entities side-by-side'), and distinguishes itself from the sibling ai_visibility_check by explicitly noting it probes each entity and ranks the results.

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 ('competitive AI-marketing audits') and a concrete example question ('does Claude know about us as well as our competitors?'). It implies the alternative of using ai_visibility_check for single-entity checks but 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.