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

The description discloses internal behavior: 'Probes each entity ... with ai_visibility_check, ranks by score, surfaces which is most/least recognized.' It also states the return format: 'ranked list with score, confidence, signal density per entity.' Annotations already cover readOnly/idempotent safety, so the description adds value beyond 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?

Four sentences, each adding distinct value: purpose (sentence 1), mechanism (sentence 2), use case (sentence 3), and output details (sentence 4). No fluff or repetition. The description is 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?

The tool has no output schema, but the description explicitly states the return values: 'ranked list with score, confidence, signal density per entity.' It covers the key aspects (what, how, when) and relies on the schema for parameter constraints (e.g., 2-8 entities). It is complete for a tool of this complexity, though it could mention potential side effects like multiple API calls or rate limits.

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%, so the baseline is 3. The description adds minimal extra meaning beyond the schema—it provides a real-world example of entities ('does Claude know about us as well as our competitors?') but does not explain models, _apiKey, or context in more detail than the schema already does.

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 begins with a clear, specific verb: 'Compare AI visibility across multiple entities side-by-side.' It then explains it probes each entity with ai_visibility_check and ranks by score, distinguishing it from the single-entity sibling tool ai_visibility_check. The example use case further clarifies its purpose for competitive audits.

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 strong context for when to use it: 'Useful for competitive AI-marketing audits' with a concrete example question. It doesn't explicitly state 'use ai_visibility_check for single-entity checks,' but the contrast with the sibling tool is implied. Lacks explicit when-not-to-use guidance, but the context is clear enough.

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