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

Annotations already cover read-only/idempotent safety, and the description adds meaningful behavioral context: it internally probes each entity with ai_visibility_check and ranks results. It does not discuss rate limits or potential external API costs, but the disclosed delegation and scoring logic go 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?

Three sentences with no fluff: the first states the core function, the second explains the method and output, and the third provides a real-world use case. Every sentence earns its place and key details are front-loaded.

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?

Even without an output schema, the description discloses the return format (ranked list with score, confidence, signal density per entity). It also explains the internal process and gives a practical scenario. Combined with full schema parameter documentation, it provides sufficient context for an agent to 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 description coverage is 100%, so the baseline is 3. The description does not add much beyond schema; it confirms entities are brand + competitors, but the schema already documents each parameter clearly. No extra meaning is provided 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 clearly states a specific action ('Compare AI visibility across multiple entities side-by-side') with a defined resource and scope. It distinguishes itself from sibling ai_visibility_check by emphasizing multi-entity comparison, ranking, and surfacing most/least recognized entities.

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 clear context with a concrete example ('does Claude know about us as well as our competitors?') and frames it as a competitive AI-marketing audit. However, it does not explicitly mention when not to use this tool or name alternative tools like compare_entities, so it lacks exclusion guidance.

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