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

Annotations declare readOnlyHint, openWorldHint, idempotentHint true and destructiveHint false. The description adds behavioral details: it probes each entity using ai_visibility_check, ranks results, and returns a list with score, confidence, signal density. It also explains that models can include Anthropic (requiring an API key) and that context disambiguates common names. No contradictions with annotations.

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 four sentences, each adding value: first sentence states purpose, second explains mechanism, third gives a concrete usage example, fourth lists output elements. It is front-loaded and concise with no unnecessary words.

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 the tool's complexity (comparing multiple entities with optional models and context) and the absence of an output schema, the description covers all parameters and describes the output format (ranked list with score, confidence, signal density). It does not cover potential errors or limits, but the tool's purpose is well-explained.

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%, but the description adds meaning beyond the schema: it explains that the first entity is treated as the 'subject' for narrative, that 'workers-ai' is the free default model, that '_apiKey' is only needed if 'anthropic' is in models, and that context disambiguates common names. This provides helpful context for parameter usage.

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's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare), resource (AI visibility), and scope (multiple entities). It also distinguishes from the sibling tool 'ai_visibility_check' by implying this is for multi-entity comparison, and mentions probing each entity with that tool.

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 question ('does Claude know about us as well as our competitors?'). It implies when to use this tool (multi-entity comparison) but does not explicitly state when not to use it or name alternatives, though the context suggests using ai_visibility_check for single entities.

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