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

Annotations already declare read-only, open-world, idempotent, non-destructive behavior. The description adds valuable behavioral context: it reveals the tool internally calls ai_visibility_check per entity, ranks results, and identifies most/least recognized entities. It also discloses the return structure (score, confidence, signal density), going well beyond the 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 three sentences, front-loaded with the primary purpose, followed by mechanics and output details. Every sentence adds distinct value without repetition, achieving high information density with zero filler.

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?

For a read-only tool with full schema coverage and a rich description, the missing output schema is compensated by explicitly listing return fields (score, confidence, signal density). The description covers process, use case, and result format, making it sufficiently complete for an agent to select and invoke correctly.

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 detailed descriptions for all four parameters, so the baseline is 3. The description does not add significant new parameter-level meaning beyond what the schema already provides; it only reiterates that entities are compared and ranked.

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 a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It distinguishes itself from siblings by explicitly mentioning it probes with ai_visibility_check and is intended for competitive AI-marketing audits, making its niche unambiguous.

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 provides clear use context ('competitive AI-marketing audits') with a concrete example, and implies the alternative for single-entity checks by stating it uses ai_visibility_check. While it doesn't explicitly say 'when not to use,' the guidance strongly implies the intended scenario versus alternatives.

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