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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context: it probes each entity with ai_visibility_check, ranks by score, identifies most/least recognized, and returns a ranked list with score, confidence, and signal density. It also notes the first entity is treated as the 'subject' for narrative, which is beyond schema.

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, front-loaded with the core purpose, followed by process, use case, and return output. Every sentence adds value with no 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?

With no output schema, the description appropriately explains the return format (ranked list with score, confidence, signal density). It covers the tool's purpose, process, entity constraints (2-8, first as subject), and an example scenario. For a read-only, non-destructive tool with full schema coverage, this is complete.

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%, so the baseline is 3. The description's mention of 'your brand + N competitors' adds context to the entities parameter, but does not meaningfully elaborate on models, _apiKey, or context beyond what the schema already states. The schema does the heavy lifting.

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, naming the specific action (probe, rank, surface) and the resource (competitive AI presence). It distinguishes itself from the sibling tool ai_visibility_check by explicitly mentioning it uses that tool per entity, and from generic compare_entities by focusing on AI visibility scores.

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 explains the mechanism (probes each entity with ai_visibility_check). It implies that ai_visibility_check is for single entities, though it does not explicitly state when not to use this tool or mention alternatives by name. The context is strong but not exhaustive.

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