Avenity AI Visibility

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

With no annotations provided, the description carries the behavioral burden. It discloses that the tool is free, is a check/read-type operation, and returns whether the business is named in AI answers. However, it does not clarify data sources, whether the response is a simple yes/no, or any limits, leaving some 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 two crisp sentences plus a one-word 'FREE' flag. The core function is front-loaded, and the workflow-context sentence earns its place by guiding when to use 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?

For a simple three-parameter lookup with 100% schema coverage and an output schema, the description supplies purpose, scope, cost, and workflow context. It could mention the exact AI source or answer format, but nothing blocks an agent from invoking 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 baseline is 3. The description adds the framing of 'money category' and 'place' which maps naturally to category and location, but it provides no additional syntax, formatting, or relationship details beyond the schema.

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?

States a specific verb ('Check whether'), a clear object (a business being named in AI answers), and the two key filters (money category, place). It also distinguishes its role by calling it the 'top-of-funnel' diagnostic step, separating it from the engagement-focused sibling tools.

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 workflow context: this is the top-of-funnel tool agents use to diagnose a client. It does not explicitly name sibling tools or say when not to use it, but the intended placement in the process is unmistakable.

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 provided, the description carries the transparency burden and handles it well by disclosing the payment gate, the wallet destination, settlement via facilitator, order recording, and engagement confirmation. It does not mention reversibility or duplicate-purchase risk, but the core side effects are explicit.

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 with purpose before the gating condition. No words are wasted, though jargon like 'x402-GATED' and 'HTTP-402-shaped' may require external context.

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 output schema and fully documented parameters, the description provides the missing behavioral state: payment gating, settlement, order recording, and confirmation. It could more explicitly explain the relationship to request_engagement_quote, but an agent has enough to call the tool 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 schema already documents all five parameters. The description reinforces the role of payment_proof but adds no new parameter-level meaning, matching the baseline for fully covered schemas.

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 clear verb and resource: 'Hire Avenity', then specifies what the tool does: settle via the facilitator, record the order, and confirm the engagement. This clearly distinguishes it from the sibling tools request_engagement_quote and audit_business_visibility.

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 x402-gating behavior gives concrete invocation context: without valid payment_proof the tool returns payment requirements, and with valid payment_proof it completes the purchase. It does not explicitly name sibling tools as alternatives, so it stops short of a 5.

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 transparency burden. It discloses that the call is free, returns scope and price, and explains tier pricing and category granularity. It does not explicitly state side effects or confirm it is read-only, but 'FREE' and 'Return the scope and price' strongly imply a non-mutating quote operation. Some behavioral details, such as whether the quote is binding or expires, are absent.

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 concise and front-loaded with the most important facts: free, returns scope/price, tier options. The pricing list is necessary and compact. It could be slightly more structured, but every sentence earns its place 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?

The tool has an output schema, so return-value details are not required. With no annotations and no usage guidance, the description covers pricing and scope but omits when-to-use guidance relative to the sibling tools and some edge-case policy (e.g., what happens if categories exceed the local tier limit). It is adequate for a straightforward quote tool but has clear gaps.

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 0%, so the description must compensate. It adds real semantics for 'tier' by listing all allowed values with prices and category allowances, and clarifies that categories are separate units of work. 'business_name' and 'categories' are given context through 'business NAMED in AI answers' and 'given categories.' Minor gaps remain around category format and tier limits, but the description does substantial work beyond the bare schema.

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: 'Return the scope and price to get a business NAMED in AI answers for the given categories.' It clearly explains what the quote covers and even defines categories as separate data-engineering units. This distinguishes it from the audit and purchase sibling tools without needing to name them.

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 implies usage: it is a free quote request for pricing and scope before engaging. However, it does not explicitly say when to use this tool instead of audit_business_visibility or purchase_engagement, nor does it state prerequisites or when not to use it. The phrase 'FREE' and the pricing details hint at the pre-purchase context but leave routing to inference.

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