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

Annotations already cover readOnly/openWorld/idempotent, but the description adds behavioral detail: it probes each entity with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, and signal density. This goes beyond annotation metadata and clarifies the internal process and output without contradicting 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 two sentences, front-loaded with the primary action, and contains no filler. The first sentence explains what and how; the second provides use case and output specification. 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?

Despite lacking an output schema, the description specifies the return format (ranked list with score, confidence, signal density), explains the process (probes with ai_visibility_check), and gives a realistic use case. The parameter semantics are well covered by the schema, and the description adds the remaining context an agent needs to know when and how to invoke it.

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 adds meaningful semantics beyond the schema by specifying that the first entity is treated as the 'subject' for narrative and the rest are competitors, which is not present in the schema description for 'entities'. This clarifies ordering and interpretation.

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 states a specific verb ('Compare') and resource ('AI visibility across multiple entities'), and explains the expected outcome (ranks by score, surfaces most/least recognized). It clearly distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic) by focusing on AI presences and using ai_visibility_check as a probe.

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 a concrete use case ('competitive AI-marketing audits') with an example query ('does Claude know about us as well as our competitors?'). It implies a multi-entity context but does not explicitly mention alternatives for single-entity checks or when not to use this tool, though the side-by-side framing suggests those boundaries.

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