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

Annotations provide readOnlyHint, idempotentHint, etc. Description adds mechanism: probes each entity with `ai_visibility_check`, ranks by score, surfaces most/least recognized. No contradiction. Good extra context 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?

Two sentences, front-loaded with purpose. Every sentence adds value: what it does, how it works, when to use, return format. No fluff.

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, description specifies return: 'ranked list with score, confidence, signal density per entity'. Covers key parameters (models, apiKey, context, entities). Complete for tool complexity.

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 100% so baseline 3. Description adds meaning beyond schema: notes first entity is 'subject' for narrative, explains models default behavior. Provides context on `context` parameter for disambiguation.

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?

Description clearly states 'Compare AI visibility across multiple entities side-by-side', specifying the verb (compare/scan), resource (competitor AI presence), and distinguishes from sibling tool `ai_visibility_check` which is for single entities. It is specific and actionable.

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: 'useful for competitive AI-marketing audits' with example question. Implicitly contrasts with single-entity tool `ai_visibility_check`. Lacks explicit 'when not to use' but strong contextual guidance.

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