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

The description adds behavioral context beyond annotations: it describes the internal workflow (probes each entity with ai_visibility_check, ranks by score), the output fields (score, confidence, signal density), and special handling (first entity as subject). This supplements the readOnly/ idempotent hints without contradiction.

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 two sentences plus a short quote, front-loaded with the main action. Every sentence adds value without repetition 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?

Given 4 parameters, no output schema, but rich annotations, the description covers the tool's purpose, workflow, parameter nuances, and expected output. It lacks details on error handling or rate limits, but for a read-only idempotent tool, this is acceptable.

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 already has 100% description coverage. The description adds extra meaning: it clarifies entities array must be 2-8, first is subject, rest are competitors; explains 'models' defaults and API key requirement; and provides context on the 'context' parameter usage. This exceeds what the schema alone provides.

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 verb+resource: 'Compare AI visibility across multiple entities side-by-side.' It distinguishes from sibling tool ai_visibility_check by explicitly mentioning multi-entity comparison and ranking, and provides a concrete use case quote.

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 implies the tool is for comparing multiple entities. It mentions that each entity is probed with ai_visibility_check, hinting that single-entity checks should use that tool directly. However, it lacks explicit when-not-to-use guidance or direct comparisons to siblings like compare_entities.

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