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

The description adds behavioral detail beyond annotations by explaining the internal process (probes with ai_visibility_check) and the output structure (ranked list with score, confidence, signal density). It doesn't cover rate limits or error handling, but annotations already declare read-only, idempotent, non-destructive safety, so the added context is sufficient.

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 value: purpose, method, use case/example, and return format. Front-loaded with the main action, no fluff or repetition of schema/annotations.

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 clearly states the return format (ranked list, score, confidence, signal density). It also explains the tool's mechanics, input constraints (2-8 entities), and use case, making it fully self-contained for an agent.

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 coverage is 100%, so baseline is 3. The description doesn't add parameter-level semantics beyond what the schema already documents, but it does reinforce the 'subjects vs competitors' structure and 'shared context' concept. No extra meaning is provided, so it stays at baseline.

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 a specific verb ('Compare AI visibility') with a well-defined outcome: side-by-side ranking across multiple entities, surfacing most/least recognized. It explicitly mentions probing with ai_visibility_check, which distinguishes it from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison).

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?

It provides clear context for when to use the tool (competitive AI-marketing audits) with a concrete example question. However, it doesn't explicitly state when not to use it or name alternatives, although the multi-entity framing implicitly contrasts with single-entity checks.

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