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

The description adds behavioral context beyond annotations: it discloses that the tool internally probes with ai_visibility_check, ranks by score, and returns ranked results with score, confidence, and signal density. This complements the readOnly/openWorld/idempotent annotations by describing the composite, rank-producing behavior. It could be more expansive about error handling or rate limits, but the provided detail is strong.

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 with no wasted words. It front-loads the purpose, then explains the mechanism, usefulness, and return value. The example quote is succinct and memorable. Every sentence earns its place.

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 the tool's moderate complexity (4 params, composite operation, no output schema), the description covers the key points: what it compares, how it works, use case, and return format. It could mention potential limitations (e.g., number of entities, model behavior), but the schema already handles entity bounds, and annotations cover safety, making the description reasonably 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 coverage is 100%, with each parameter already described clearly. The description adds semantic value by noting that the first entity is treated as the 'subject' for narrative, and the rest are competitors, which is not fully evident from the schema alone. It also ties models and _apiKey together implicitly, reinforcing their relationship.

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's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the action (compare), resource (AI visibility), and scope (multiple entities), and distinguishes from sibling tools like ai_visibility_check by focusing on multi-entity comparison. The example 'does Claude know about us as well as our competitors?' encapsulates the intent.

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 clear context for when to use the tool ('Useful for competitive AI-marketing audits') and implies the alternative: since it probes each entity with ai_visibility_check, a user needing a single entity check should use that sibling tool instead. However, it does not explicitly state 'use this instead of X' or list exclusions, so it falls short of a 5.

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