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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description legitimately focuses on additional behavior: it explains that gap_confirmed=True means the business is NOT appearing and that this is the core hook for Avenity's service. It also details what the return payload contains, giving the agent useful expectations beyond the 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 well-structured and front-loaded: a clear one-sentence purpose, then use cases, args, returns, and examples. Every section adds practical value, and the natural-language examples help an agent map user requests to the tool.

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 moderate complexity, the description covers purpose, usage timing, parameter shape, return semantics, and example phrasings. It is nearly complete, though it does not mention error conditions or how the optional website_url changes behavior.

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 low, so the description must carry some param meaning. It names the key inputs ('Business details including name, service, city') and provides natural-language examples, but it does not explain optional fields like state or website_url, nor how service and city are combined into the search query. This partially compensates but leaves gaps.

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 and resource: 'Check whether a business appears in AI-driven search results when buyers search for their primary service in their city.' It clearly defines the tool's scope and reinforces it with the 'first-pass qualifier' framing, which distinguishes it from sibling tools like avenity_score_business or avenity_get_opportunity_report.

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 explicit 'Use this when' scenarios, such as confirming an AI visibility gap before recommending action or framing the problem before showing a full report. It does not explicitly name sibling tools as alternatives or state when not to use it, so it stops short of full routing guidance.

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