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

Annotations cover the read-only/idempotent nature, so the bar is lower. The description adds meaningful behavioral context by explaining the internal action ('Probes each entity ... with ai_visibility_check'), the ranking behavior, and the exact output fields ('score, confidence, signal density per entity'). This goes beyond the structured annotations without contradicting them.

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 a tight three sentences: purpose, mechanics, and use case. Every sentence contributes new information, with no repetition of the tool name or obvious fluff. It's front-loaded with the primary verb and resource.

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?

With 4 parameters but no output schema, the description compensates by explicitly stating the return format: 'ranked list with score, confidence, signal density per entity'. Combined with annotations that confirm safety, and the schema that documents parameters, the description provides complete operational context for an AI agent to select and invoke the 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?

The schema already documents all 4 parameters with 100% coverage, giving the baseline 3. The description adds a crucial semantic detail: the first entity is the 'subject' and the rest are competitors, which informs how the entity array should be constructed. It also clarifies that the 'context' parameter is applied to every probe, adding interpretive value.

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 opens with 'Compare AI visibility across multiple entities side-by-side', which clearly states the verb, resource, and scope. It further distinguishes from the sibling ai_visibility_check by noting it probes each entity with that tool and ranks them, making its multi-entity purpose unambiguous. This is a specific, non-tautological description.

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' with the example question 'does Claude know about us as well as our competitors?'. It also implies that for a single entity, ai_visibility_check is the appropriate tool, but it doesn't explicitly state when not to use this tool or name alternatives beyond the mechanism. That gap prevents a 5.

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