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

Annotations already declare readOnly, idempotent, non-destructive, open world. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. This provides useful behavioral context beyond annotations, though it does not detail error handling or rate limits.

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 to three sentences, front-loaded with the main purpose, followed by mechanism, use case, and output details. Every sentence adds value with 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?

For a tool with four parameters and no output schema, the description covers purpose, method, use case, output structure, and parameter ordering. It is self-contained and informative enough for an agent to understand when and how to invoke 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 description coverage is 100%, but the description adds valuable semantics: 'First entry treated as the 'subject' for narrative; rest are competitors.' It also clarifies the models parameter with supported values and API key requirement. This goes beyond the schema alone.

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 compares AI visibility across multiple entities side-by-side, uses ai_visibility_check, ranks by score, and identifies most/least recognized. It distinguishes from the sibling tool 'ai_visibility_check' by being a batch comparison and from 'compare_entities' by being AI-presence specific.

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 concrete use case (competitive AI-marketing audits) and an example question. It implicitly indicates when to use (comparing multiple entities) but does not explicitly state when not to use or mention alternatives like using 'ai_visibility_check' for a single entity.

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