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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by explaining it internally calls 'ai_visibility_check' for each entity, ranks by score, and returns a ranked list with score, confidence, and signal density. This goes beyond the annotations without contradicting them.

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 concise at four sentences, with the primary action front-loaded. Every sentence adds value: purpose, internal operation, use case, and output format. No redundant or irrelevant information.

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 the tool's moderate complexity (composite probe) and good annotations, the description sufficiently covers what the tool does, how it works, and what it returns. Though there is no output schema, the description lists the returned fields (score, confidence, signal density). No missing information is critical for an agent to use the tool correctly.

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 baseline is 3. The description adds meaning by explaining that the first entity in the array is treated as the 'subject' for narrative, and that 'context' is applied to every probe. This provides context beyond the schema's parameter descriptions.

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 a specific verb ('Compare AI visibility across multiple entities side-by-side') and resource, and distinguishes from sibling 'ai_visibility_check' by clarifying it runs multiple probes and ranks results. It also contrasts with potential alternatives like 'compare_entities' by specifying the focus on AI visibility.

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: useful for competitive AI-marketing audits. It implicitly suggests when to use (multi-entity comparison) versus the sibling 'ai_visibility_check' (single entity), but does not explicitly state when not to use or give alternative tools. Still, the usage scenario is well-defined.

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