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

Discloses that it internally calls ai_visibility_check for each entity and returns a ranked list with score, confidence, signal density. Annotations already declare readOnly, idempotent, non-destructive, and open world; description adds context about batch behavior and ranking. No contradictions, and adds value beyond 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?

Three sentences with clear hierarchy: purpose, mechanism, example. Front-loaded, no fluff. Every sentence earns its place.

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?

Covers input parameters and return values (ranked list with metrics) despite no output schema. For a composite tool, it explains internal calls and ranking. Lacks error handling or edge cases, but overall sufficient for agent selection and basic invocation.

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 has 100% description coverage, so baseline is high. Description adds meaning: 'First entry treated as the subject for narrative; rest are competitors' and clarifies default models and optional Anthropic key. This provides crucial context not in 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?

Clearly states the tool compares AI visibility across multiple entities side-by-side, describes the process (probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized), and gives a concrete usage example. Distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (more general).

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?

Provides explicit use case: competitive AI-marketing audits, with an example question. Implies when to use (comparing multiple entities) and treats first entity as narrative subject. However, does not explicitly state when not to use or list alternative tools, though sibling differentiation is clear enough.

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