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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true. The description adds meaningful behavioral context: it probes each entity with ai_visibility_check, returns a ranked list with score, confidence, and 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 efficient sentences: first states core action, second explains the underlying mechanism, third provides usage context and return value. Every sentence earns its place with no redundancy.

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 with full schema coverage and no output schema, the description adequately covers the return format (ranked list with score, confidence, signal density) and explains the models parameter. Could mention result ordering or error handling, but sufficient for a well-annotated 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% (baseline 3). The description adds value beyond schema by explaining that the first entity in the array is treated as the 'subject' for narrative, and the context parameter disambiguates common names.

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 explicitly states it compares AI visibility across multiple entities using ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from the single-entity sibling ai_visibility_check and the general compare_entities.

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 use case (competitive AI-marketing audits) and an example question. It implies when to use (multiple entities) but does not explicitly state when not to use it or mention alternatives, though the sibling tool ai_visibility_check is available for single entities.

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