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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds beyond this: it explains that each entity is probed with ai_visibility_check, results are ranked by score, and it surfaces most/least recognized. It also details the return format (ranked list with score, confidence, signal density). No contradictions.

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, all value-adding. The first sentence immediately states the core function. No verbose or redundant text. Every sentence earns its place.

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?

Without an output schema, the description sufficiently explains the return value (ranked list with score, confidence, signal density). It covers the tool's purpose, usage, and key parameters. A small gap is the lack of mention of the optional 'models' parameter's effect on results, but overall it is complete given the tool's complexity.

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% and descriptions are provided. The description adds marginal value: it notes the first entity is treated as 'subject', and context disambiguates common names. However, these are already implied or described in the schema. Baseline 3 is appropriate as the description does not significantly enhance schema parameters.

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 uses specific verbs ('Compare', 'Probes', 'ranks') and clearly identifies the resource ('AI visibility across multiple entities'). It differentiates from sibling tool ai_visibility_check (single entity) and implies comparison versus general compare_entities. The example question clarifies the exact use case.

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 explicitly states when to use: for competitive AI-marketing audits. It provides an example question and mentions the tool compares multiple entities. While it doesn't explicitly list exclusions, the context signals and sibling tools suggest alternatives. The description is clear enough for agents to select correctly.

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