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

Annotations already declare read-only, idempotent, non-destructive, and open-world. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns a list with score, confidence, and signal density. No contradictions.

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?

Two concise sentences: first covers core action and output, second gives use case and example. No unnecessary words. Very efficient.

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 explains the transformation (calling ai_visibility_check, ranking) and output format (ranked list with metrics). It notes the first entity as subject. Lacks exact output structure but is sufficient for an agent to infer. No output schema, so description carries weight, but it is 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 description coverage is 100%, so baseline is 3. The description adds context about how parameters are used (e.g., first entity as subject, probes per entity), which provides moderate added value beyond schema descriptions.

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, probes each with ai_visibility_check, and ranks results. It distinguishes from siblings like ai_visibility_check by specifying multi-entity comparison. The use case is explicitly given.

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 explains that the tool is useful for competitive AI-marketing audits and provides an example. It implicitly tells when to use it instead of calling ai_visibility_check multiple times. However, it does not explicitly state when not to use or list alternative tools, but the context is clear.

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