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

Annotations already indicate safe, read-only, idempotent behavior. The description adds that it probes with ai_visibility_check, ranks results, and returns per-entity fields (score, confidence, signal density). No contradictions; additional behavioral context is provided.

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 with no wasted words. The core action ('compare AI visibility'), internal process (calls ai_visibility_check), output summary, and use case are all front-loaded and 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?

Given no output schema, the description appropriately describes the return format (ranked list with score, confidence, signal density per entity). It explains the core process. Minor missing details like ranking algorithm or score interpretation, but overall sufficient for safe use.

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 descriptions cover all 4 parameters. The description enriches meaning by stating the first entity is treated as the 'subject' for narrative, and that 'context' disambiguates common names. This adds value beyond the schema alone.

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 verb 'compare' and the resource 'AI visibility across multiple entities'. It references the internal call to 'ai_visibility_check', distinguishing it from that single-entity sibling. Both purpose and differentiation are explicit.

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 gives a concrete use case ('competitive AI-marketing audits') and an example query. It implies when to use but does not explicitly state when not to use nor compare with siblings like 'compare_entities'. Still, the guidance is clear enough for most scenarios.

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