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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context: it probes each entity with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, and signal density. It also notes first entity is subject for narrative, which aligns with schema. No contradiction.

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, front-loaded with the core purpose. Each sentence adds value: mechanism, use case, and output format. No redundancy or fluff.

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 that the schema documents all parameters and the tool has no output schema, the description provides sufficient context: what it does, how it works, when to use it, and what the return looks like. It covers the essential information for an agent to select and invoke it effectively.

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 adds minimal parameter-specific meaning beyond the schema: it references 'your brand + N competitors' but the schema already explains entities fully. No extra clarification for models, _apiKey, or context.

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, specifically using ai_visibility_check and ranking by score. This distinguishes it from siblings like ai_visibility_check, which likely handles single entities, and compare_entities, which may be more general.

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?

It explicitly frames usage for competitive AI-marketing audits with an example ('does Claude know about us as well as our competitors?'). It implies using this when comparing multiple entities, but does not explicitly name alternatives or state when not to use it. Thus, clear context but no explicit exclusions.

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