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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond that: it discloses that the tool makes multiple probes, ranks results, and specifies the output fields (score, confidence, signal density). This goes beyond the structured data and clarifies the orchestration behavior.

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 four sentences, front-loaded with the primary action ('Compare AI visibility across multiple entities side-by-side'). Each sentence adds a distinct piece of value: action, mechanism, use case, and return format. There is no filler 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?

With no output schema, the description appropriately covers return value details (ranked list with score, confidence, signal density) and explains the purpose with a concrete use case. It also references ai_visibility_check, linking to the sibling tool. While it does not mention rate limits or credential nuances, those are in the schema, making this generally sufficient for a multi-entity comparison 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 descriptions for all parameters, including the rule that the first entity is the subject and the rest are competitors. The description adds no new parameter-level information; it mentions 'your brand + N competitors' but this is already in the schema. Thus it meets the baseline without going beyond.

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: 'Compare AI visibility across multiple entities side-by-side' and explains the process: probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It explicitly distinguishes itself from the sibling ai_visibility_check by focusing on multi-entity comparison.

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 for competitive AI-marketing audits with an example query, and the mention of ai_visibility_check implies the single-entity alternative. However, it does not explicitly state 'use ai_visibility_check for single entities' or list exclusions, so there is implicit but not fully explicit guidance.

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