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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with confidence and signal density. No contradictions; it complements the annotations with useful operational context.

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 exactly two sentences, front-loaded with the main action, and every sentence adds value. It states the core function, the method (probes with ai_visibility_check), the output, and a concrete use-case example. No redundancy or filler.

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?

No output schema exists, but the description explicitly names the return fields ('ranked list with score, confidence, signal density per entity'), which directly addresses what the agent will get. The complexity is moderate (4 parameters, 1 required), and the description covers the workflow and purpose sufficiently for an agent to decide when to use it.

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 description coverage is 100%, and each parameter (models, _apiKey, context, entities) already has a clear description. The tool description adds only the narrative that the first entity is the 'subject' and others are competitors, which is a slight enhancement over the schema's 'First entry treated as the subject...' but not substantial beyond that. Baseline 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's purpose: 'Compare AI visibility across multiple entities side-by-side.' It names the specific verb ('Compare'), the resource (AI visibility across entities), and distinguishes from siblings by mentioning it probes with ai_visibility_check and ranks results. The example query clarifies its 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 provides a clear context: 'Useful for competitive AI-marketing audits' and gives an example question. It does not explicitly mention when not to use it or name alternatives, but the contrast with the single-entity sibling ai_visibility_check is implied through the phrase 'across multiple entities.'

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