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

Annotations already indicate safe read-only behavior. The description adds valuable context by revealing it internally calls ai_visibility_check per entity, ranks by score, and returns specific fields (score, confidence, signal density). This goes beyond the annotation baseline and provides useful behavioral insight without contradiction.

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 long, front-loaded with the main purpose, and every sentence earns its place: the first defines the action, the second explains mechanics, and the third gives a concrete use case and return value. 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?

Without an output schema, the description compensates by explicitly naming the return payload ('ranked list with score, confidence, signal density per entity'). It also explains the internal workflow and a realistic usage scenario. Minor omissions like error handling or interpretation of confidence are not critical for a read-only comparison tool.

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%, and each parameter has a meaningful description in the schema (e.g., entities array, optional models, context disambiguation). The tool description itself does not add extra parameter-level detail beyond what the schema already provides, so the baseline of 3 applies.

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 verb+resource combo: 'Compare AI visibility across multiple entities side-by-side.' It further distinguishes itself from the sibling tool ai_visibility_check by explicitly mentioning it probes each entity with that underlying tool and ranks results, making it clear this is a comparative multi-entity version.

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 gives a clear use case ('competitive AI-marketing audits') and even quotes an example question. It implies this tool is for comparing multiple entities, which distinguishes it from the singular ai_visibility_check, but it does not explicitly state when not to use it or name alternatives.

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