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

Annotations already provide read-only/idempotent hints. The description adds valuable process details: calls ai_visibility_check internally for each entity, ranks by score, treats the first entity as the subject, and returns specific fields (score, confidence, signal density). This goes beyond the structured annotations but doesn't cover all edge cases like rate limits.

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 plus an illustrative quote. The main verb appears immediately, and every clause adds information: mechanism, ranking, return fields, use case. No tautology or 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?

Given no output schema, the description clearly states return format ('ranked list with score, confidence, signal density per entity'), internal mechanism, and the special treatment of the first entity. Complexity is moderate and all key aspects are covered. The example query further contextualizes when to use 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% with descriptive parameter texts, so the baseline is 3. The description doesn't add significant meaning beyond the schema beyond framing entities as 'your brand + N competitors,' which is already implied by the schema's mention of 'subject.' No extra semantics for models, _apiKey, or context.

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 opens with a specific action 'Compare AI visibility across multiple entities side-by-side,' identifies the internal mechanism (probes with ai_visibility_check), and differentiates from siblings like ai_visibility_check (single-entity vs multi-entity). It names the output type and ranking behavior.

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?

It identifies a clear use case ('competitive AI-marketing audits') with an example query, and implies comparison-oriented usage distinct from single-entity checks. However, it doesn't explicitly state exclusions or when not to use this tool vs alternatives.

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