Avenity AI Visibility

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

With no annotations, the description carries the behavioral burden. It adds the 'FREE' cost signal and makes the read-only nature clear via 'Check whether.' It could mention response format or side effects, but the output schema exists and the operation is straightforwardly non-mutating.

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?

Two sentences, no filler. The key action and context are front-loaded, 'FREE' is immediately visible, and every sentence adds value.

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 tool is simple, schema covers all parameters, and an output schema exists. The description adds needed context about when to use it. It does not discuss limitations or relationships to siblings in depth, but it is sufficient for an agent to invoke it 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 parameters are already well documented with examples. The description does not add extra parameter-level meaning beyond the schema, which is acceptable given the baseline for full coverage.

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 a specific verb ('Check whether') and names the exact resource and scope: whether a business is named in AI answers for a given money category and place. This clearly distinguishes it from the purchase-oriented sibling tools, which focus on engagement and quotes.

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 clear use context: 'top-of-funnel' and 'agents run it to diagnose a client.' It does not explicitly list exclusions or when to prefer a sibling, but the context is strong enough for an agent to know this is an initial diagnostic step.

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

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

With no annotations, the description carries the full burden, and it is transparent about the x402 gate, the HTTP-402-shaped response without valid payment_proof, payment verification against the facilitator, and the fact that 'acquired' is only set after on-chain settlement. It could add more about edge cases like failed/invalid proof or idempotency, but the core behavior is well disclosed.

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 compact and front-loaded: one imperative hook ('Hire Avenity.') followed by a dense but efficient explanation of the payment gating and settlement behavior. Every sentence earns its place; there is no filler.

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?

For a payment-gated purchase tool with no annotations and an output schema present, the description explains the critical flow well: what happens without payment_proof, what happens with it, and when an engagement is considered acquired. It is missing only explicit routing guidance against the sibling tools, which prevents a perfect score.

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?

The schema already describes all 5 parameters with 100% coverage, so the baseline is 3. The description adds real behavioral meaning to payment_proof by explaining that it is the gate and settlement payload, and the tier parameter schema points to request_engagement_quote for pricing context. This goes beyond mere schema repetition.

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 opening phrase 'Hire Avenity' identifies a specific action and object, and the rest of the description clarifies this is the paid acquisition step. It is not a tautology and is distinguishable from the sibling tools by its payment-gating focus, though it could state more directly that it purchases/confirms an engagement.

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 explains two usage modes: without payment_proof it returns payment requirements, and with payment_proof it verifies/settles the payment before confirmation. This implicitly positions the tool as the settlement step after request_engagement_quote, but it never explicitly says when to use this vs audit_business_visibility or when not to call it.

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

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

No annotations are present, so the description carries the behavioral burden. It usefully discloses that the tool is free, returns scope/price, and treats each category as a separate unit of work. However, it does not explicitly state whether any record is created or whether this defers commitment/purchase, which leaves some side-effect ambiguity.

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 compact, front-loaded with the main purpose, and uses a scannable tier list. Every sentence adds useful information and there is no filler.

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?

An output schema exists, so return-value details are not required from the description. The description covers pricing, tier options, and category semantics; the main gaps are explicit guidance on category-count limits per tier and the slightly opaque 'paige' tier explanation.

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?

With 0% schema coverage, the description compensates well by enumerating the tier values with prices and explaining category semantics. Business_name is implied by 'business NAMED in AI answers', but the description does not specify category-count limits for regional/national tiers or mention the 'local' default.

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?

Description states a clear verb and resource: 'Return the scope and price to get a business NAMED in AI answers for the given categories.' This distinguishes it from purchase_engagement (quote vs purchase) and audit_business_visibility, while the tier/pricing detail reinforces the quoting purpose.

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 opening 'FREE' and 'Return the scope and price' clearly position this as the pre-purchase quoting step relative to purchase_engagement. It does not explicitly name when-not-to-use alternatives, so it stops short of full exclusion guidance.

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