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

Annotations already declare readOnly and idempotent behavior. The description adds value by explaining the internal process (probes each entity with ai_visibility_check, ranks results) and output details (score, confidence, signal density). This goes beyond the 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?

The description is 3-4 sentences with the main purpose front-loaded in the first sentence. Every sentence adds value: purpose, internal mechanism, use case, return type. No fluff or repetition.

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?

Given no output schema, the description fairly covers what the tool does and returns (ranked list with score, confidence, signal density). It could be more explicit about the exact output structure, but it is adequate for understanding usage.

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 mild context (first entity as subject, models optional, context for disambiguation) but does not significantly expand on what the schema already provides.

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 the tool compares AI visibility across multiple entities side-by-side using ai_visibility_check, ranks by score, and identifies most/least recognized. It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic) by specifying the process and competitive audit use case.

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 explicitly says it is useful for competitive AI-marketing audits and implies when to use: when comparing multiple entities. It does not list explicit alternatives or exclusions, but context signals from sibling tools (ai_visibility_check for single entity) provide enough guidance.

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