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

Annotations already declare read-only, idempotent, open-world, and non-destructive. The description adds valuable behavioral details: it probes each entity, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, and signal density. It also reveals the 'first entry as subject' convention. No contradictions with annotations.

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 sentences, front-loaded with purpose, then process, then usage context and return format. Every sentence contributes value with no repetition 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?

No output schema exists, so the description compensates by explaining the return structure (ranked list with score, confidence, signal density). It covers the multi-entity comparison process and the optional API key for Anthropic models, though it does not detail error behavior or rate limits, which are less critical for a read-only, idempotent 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 coverage is 100%, so baseline is 3. The description adds meaningful semantics for the 'entities' parameter by specifying the first entry is the subject and the rest are competitors. This goes beyond the schema's basic 'array of entities' description, providing narrative context that affects how the tool interprets input.

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 a specific verb (Compare) and resource (AI visibility across multiple entities side-by-side), and clearly distinguishes from siblings by explaining it probes with ai_visibility_check and ranks results. This differentiates it from the single-entity ai_visibility_check tool and generic 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?

Provides clear context for when to use it (competitive AI-marketing audits) with a concrete example query. It does not explicitly name alternatives or when not to use it, but the 'side-by-side' comparison and mention of the underlying probe make the intended use clear.

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