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

Adds meaningful behavior beyond annotations: it probes each entity via ai_visibility_check, ranks by score, surfaces most/least recognized, and returns score, confidence, and signal density. Also discloses external API usage for anthropic. Annotations already cover read-only/idempotent safety, so the description enriches rather than repeats.

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 well-structured sentences with a front-loaded purpose statement. Every sentence contributes: mechanism, use case, and output format. No fluff or repetition of schema content.

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 lacking an output schema, the description clearly states the return elements (ranked list, score, confidence, signal density). It covers the use case, probe method, optional model handling, and entity role, making it sufficiently self-contained for invocation decisions.

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 the baseline applies. The description adds context (e.g., 'your brand + N competitors') but does not significantly expand on the schema's parameter descriptions. It mirrors rather than deepens the semantic meaning.

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 'Compare AI visibility across multiple entities side-by-side' with a specific verb, resource, and scope. Names the underlying probe (ai_visibility_check) and the output (ranked list), distinguishing it from single-check sibling tools.

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?

Explicitly frames the tool for competitive AI-marketing audits and provides a concrete example. It implies when to use it (multi-entity comparison) vs. the single-entity ai_visibility_check, but does not explicitly name alternatives like compare_entities or state when NOT to use it, leaving a small gap.

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