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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the tool is safe and idempotent. The description adds behavioral context: it probes each entity with ai_visibility_check, ranks results, and returns a list with score, confidence, and signal density. It does not mention rate limits or auth details beyond the optional _apiKey, but these are covered in parameters.

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 three sentences, well-structured, and front-loaded with the main purpose. Each sentence adds value: purpose, operational detail, and usage context. No unnecessary words or 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?

The description covers input constraints (2-8 entities, first is subject) and output format (ranked list with score, confidence, signal density per entity). It does not discuss error conditions or invalid inputs, but with 100% schema coverage and clear annotations, the definition is adequately complete for an agent.

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%, all parameters have descriptions. The description adds value beyond the schema: it clarifies that the first entity in the array is treated as the 'subject' for narrative, and explains the default for models (workers-ai) and when _apiKey is needed. This extra context helps the agent use the tool correctly.

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 states it compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces which is most/least recognized. It gives a concrete example ('does Claude know about us as well as our competitors?') and distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (general comparison).

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 says it is 'useful for competitive AI-marketing audits' and provides an example query. It implies that for a single entity, one should use ai_visibility_check, but does not explicitly state when not to use this tool or name alternatives. The context is clear but lacks explicit exclusions.

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