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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds that it returns a ranked list with score, confidence, and signal density per entity, and that it probes each entity with ai_visibility_check. This aligns with annotations and provides extra behavioral context.

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 sentences: first states purpose, second gives use case and output details. No unnecessary words, well front-loaded with the core action.

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?

Despite no output schema, the description explains the return format (ranked list with score, confidence, signal density). It also describes the probing mechanism. For a comparative tool with 4 parameters, this is 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% (all 4 parameters have descriptions). The description adds semantics: 'context' disambiguates common names, 'entities' first entry as subject, and 'models' defaults to workers-ai. This adds meaning beyond the schema.

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 resource 'AI visibility across multiple entities side-by-side'. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the competitive AI-marketing audit use case.

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 usage context with the example 'does Claude know about us as well as our competitors?' and mentions it probes each entity using ai_visibility_check. While it doesn't explicitly state when not to use, the context is clear enough for an agent to infer appropriate use cases.

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