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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds value by disclosing that the tool 'probes each entity with ai_visibility_check,' explaining the internal mechanism, and specifying the output (ranked list with score, confidence, signal density). No contradiction 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 three sentences: main purpose, methodology, and use case/output. Every sentence adds value, front-loaded with the most important information. No wasted words.

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?

The description covers all necessary aspects: what the tool does, how it works (calling another tool), what parameters mean, output format (ranked list with metrics). With full schema coverage and annotations, the description is complete and sufficient for an agent to select and invoke the tool 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%, and the description adds critical meaning: entities are listed with the first as the 'subject' for narrative, models explain default vs. apiKey requirement, and context is optional for disambiguation. This goes beyond the schema descriptions by clarifying usage semantics.

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's purpose: 'Compare AI visibility across multiple entities side-by-side.' It explains the process by probing each entity with ai_visibility_check, ranking by score, and surfacing most/least recognized. This distinguishes it from siblings like ai_visibility_check (single entity) and compare_entities (general comparison).

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 a clear use case: 'competitive AI-marketing audits: does Claude know about us as well as our competitors?' It implies when to use (multi-entity comparison) and implicitly suggests ai_visibility_check for single entity checks. However, it does not explicitly list alternatives or state when not to use.

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