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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond these: it explains the tool probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with specific metrics (score, confidence, signal density). It also adds nuance about model selection and API key requirements, which is valuable for understanding side effects and dependencies.

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 and front-loaded: the first sentence states the core function, the second explains the mechanism, the third gives a use case, and the fourth lists the returns. Every sentence adds value, with no fluff or repetition. It is tightly written and appropriately sized for the tool's complexity.

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?

There is no output schema, so the description must explain return values, which it does: 'Returns ranked list with score, confidence, signal density per entity.' It also covers the use case, the probing mechanism, and parameter nuances like model selection and shared context. Given the moderate complexity and strong annotations/schema, the description is sufficiently complete for an agent to invoke and interpret the 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%, so the baseline is 3. The tool description adds some contextual meaning (e.g., 'your brand + N competitors' aligns with the schema's first-entity-as-subject note), but it largely repeats what the schema already documents. The description does not add significant new parameter-level semantics beyond the schema, so it stays at the baseline.

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.' It uses a specific verb ('Compare') and resource ('AI visibility'), and distinguishes itself from siblings by explicitly mentioning it probes with ai_visibility_check and ranks results, which is a unique feature among the listed siblings.

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 clear context for when to use the tool: 'Useful for competitive AI-marketing audits' with an example query. It implies the multi-entity comparison use case but does not explicitly exclude alternatives like using ai_visibility_check for single entities or compare_entities for general comparisons. It gives good contextual guidance but lacks explicit 'when not to use' statements.

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