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

The description discloses that the tool probes each entity with ai_visibility_check and returns a ranked list with score, confidence, and signal density—useful behavioral details beyond the annotations, which only declare read-only, idempotent, and non-destructive hints. It also implies multiple underlying calls, which is relevant for cost/latency expectations.

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, then mechanism, use case, and output. Every sentence adds value with no redundancy or unnecessary detail.

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 tool has no output schema, but the description explicitly lists the return fields (score, confidence, signal density). It covers input, mechanism, and use case. It could mention that the first entity is treated as the 'subject', but that is already in the schema, so this is complete enough for a read-only tool with strong annotations.

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 all four parameters well-described. The description adds some framing (your brand + N competitors) but does not meaningfully enhance parameter understanding beyond the schema. Baseline 3 is appropriate because the schema carries the parameter semantics.

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, ranks them, and surfaces the most/least recognized. It distinguishes itself from the sibling tool ai_visibility_check by explicitly stating it probes each entity with that tool, and from compare_entities by focusing specifically on AI visibility rather than generic comparison.

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 clear use case ('competitive AI-marketing audits') and the question 'does Claude know about us as well as our competitors?' implies when to use this tool versus single-entity checks. It does not explicitly list exclusions or alternatives, but the context is sufficient for an agent to infer appropriate usage.

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