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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds valuable behavioral details: it internally calls 'ai_visibility_check' per entity, treats the first entity as the subject, and returns a ranked list with score, confidence, and signal density. It also notes that the 'anthropic' model requires an API key. These enrich the agent's understanding of what the tool does without contradicting 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 extremely concise: 4 sentences that front-load the primary action, explain the mechanism, provide a concrete use case with an example, and summarize the output. Every sentence adds value, and there is no redundancy 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 the tool's complexity (4 parameters, no output schema), the description adequately explains the input semantics and output structure ('ranked list with score, confidence, signal density'). It mentions the entity count limit and the optional API key. The only gap is the lack of explicit error handling or edge case behavior, but this is acceptable for a comparison tool with clear annotations.

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?

While the input schema covers all 4 parameters with descriptions (100% coverage), the description adds context that the 'entities' parameter's first entry is treated as the subject and the rest as competitors. It also explains the purpose of the 'context' parameter (disambiguation) and the 'models' parameter (probe options). This goes beyond the schema alone and helps the agent use the parameters correctly.

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 uses specific verbs ('Compare', 'Probes', 'ranks', 'surfaces') and clearly states the tool compares AI visibility across multiple entities, ranks them, and identifies most/least recognized. It explicitly distinguishes itself from the sibling 'ai_visibility_check' by framing it as a multi-entity comparison tool, and the example quote reinforces the competitive audit use case.

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') and an example question, helping agents know when to invoke this tool over alternatives. However, it does not explicitly state when NOT to use it or mention the related sibling 'compare_entities', which could be a potential alternative. The guidance is strong but not exhaustive.

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