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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: it probes each entity using ai_visibility_check, ranks by score, and surfaces most/least recognized. No contradictions with 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 plus a brief clause, all front-loaded. Every sentence adds value: the first states the core action, the second explains the process and use case. No redundant information.

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 explains the return format: ranked list with score, confidence, and signal density per entity. It also mentions the internal use of ai_visibility_check. It could briefly note constraints (e.g., entity count 2-8) or error conditions, but overall is sufficient for an agent to decide and invoke.

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% with clear descriptions for all 4 parameters. The description adds extra meaning: it notes that the first entity is treated as the 'subject' for narrative, and gives examples for 'context' (e.g., 'B2B SaaS'). This enhances understanding beyond schema alone.

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 uses a specific verb-resource pair: 'Compare AI visibility across multiple entities side-by-side.' It clearly distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison). The example 'does Claude know about us as well as our competitors?' clarifies the exact 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 states it is 'Useful for competitive AI-marketing audits' and provides an example query. It implies use over ai_visibility_check when comparing multiple entities, but does not explicitly state when not to use or list alternatives. Minor improvement could include direct comparison to siblings.

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