Hireable AI-visibility service: get a business named in AI answers. Free audit + x402 hire.
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Average 3.9/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: audit checks visibility, request_engagement_quote provides pricing, and purchase_engagement executes the purchase. No overlap between their functions.
All tool names follow a consistent verb_noun pattern with snake_case: audit_business_visibility, purchase_engagement, request_engagement_quote. Naming is uniform and predictable.
With only 3 tools, the set is lean but covers the core workflow of diagnose-quote-buy. It could be slightly expanded (e.g., for monitoring), but the count is reasonable for the service's scope.
The main lifecycle (audit, quote, purchase) is covered, but there are no tools for post-purchase actions like checking engagement status or cancellation. Some minor gaps exist.
audit_business_visibilityAInspectFREE. Check whether a business is named in AI answers for a specific money category in a specific place. This is the top-of-funnel: agents run it to diagnose a client.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | The specific buyer category / product line (e.g. "commercial janitorial"). | |
| location | Yes | City/region the buyers are in (e.g. "Conroe, TX"). | |
| business_name | Yes | The business being audited (e.g. "Gracey's Commercial Cleaning"). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions 'FREE' (cost) and 'diagnose a client' (purpose), but with no annotations, more behavioral details (e.g., rate limits, auth, idempotency) would be beneficial. It adds some transparency beyond the schema.
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?
Extremely concise: two short sentences, front-loaded with 'FREE' and clear purpose. No wasted words; 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?
Given 3 required parameters with schema coverage, no enums, and an output schema, the description provides purpose and usage context. It does not detail return structure, but output schema handles that. Adequate for this tool's complexity.
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 has 100% coverage with clear descriptions for all three parameters. The description does not add parameter-specific information beyond the schema, so baseline score 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 uses a specific verb 'Check whether' and resource 'business is named in AI answers', with clear scope (money category, place). It distinguishes from sibling tools (purchase_engagement, request_engagement_quote) which focus on different actions.
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?
Explicitly states 'This is the top-of-funnel: agents run it to diagnose a client', providing clear context for when to use. Does not explicitly state when not to use or alternatives, but the usage context is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
purchase_engagementAInspectHire Avenity. x402-GATED: without valid payment_proof this returns HTTP-402-shaped payment requirements (pay to Avenity's wallet). With valid payment_proof, it settles via the facilitator, records the order, and confirms the engagement.
| Name | Required | Description | Default |
|---|---|---|---|
| tier | No | pricing tier (see request_engagement_quote). | local |
| contact | No | optional human contact for onboarding. | |
| categories | Yes | the categories/product lines to get named for. | |
| business_name | Yes | client being engaged. | |
| payment_proof | No | the x402 payment payload/settlement token from the agent's wallet. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description carries full burden. It explains the x402 gating and two outcomes, which is helpful. However, it does not disclose side effects (e.g., irreversibility, record creation) or authorization requirements beyond payment.
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 wasted words, front-loaded with action. Efficient and clear.
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?
Output schema exists, so return values are not needed. Description covers the core behavior and gating. However, it omits prerequisite context (e.g., need for a prior quote) and does not mention the expected output format or confirmation steps. Still, for a gated purchase tool, it is reasonably complete.
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 coverage is 100% (all parameters described); baseline 3. The description adds value by explaining the conditional role of payment_proof (gated vs settlement), which is not fully captured in the schema's static description. This raises the score.
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 clearly states the tool hires Avenity (specific verb and resource) and explains the two distinct behavioral paths based on payment_proof. It effectively distinguishes from siblings audit_business_visibility and request_engagement_quote by focusing on the purchase/engagement settlement action.
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?
Description provides context on when to use (with or without payment_proof) but does not explicitly state when not to use or mention alternatives like request_engagement_quote. The sibling-tools list exists, but the description lacks comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_engagement_quoteAInspectFREE. Return the scope and price to get a business NAMED in AI answers for the given categories. Each category is a separate entity / data-engineering unit of work.
tier: one of 'local' ($1500/mo, 3 categories), 'regional' ($3000/mo), 'national' ($5000/mo), or 'paige' ($300/mo monitoring/local).
| Name | Required | Description | Default |
|---|---|---|---|
| tier | No | local | |
| categories | Yes | ||
| business_name | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, but the description reveals the tool is free, returns scope and price, and details pricing tiers and category constraints. This sufficiently discloses the tool's behavior.
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?
Description is well-structured with a clear start ('FREE.') and an inline price list. While slightly lengthy, it is efficient and front-loaded.
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 output schema exists, return values need not be explained. Parameter explanations are sufficient, and the reference to categories as 'data-engineering units' provides needed context.
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 has 0% description coverage; description compensates by explaining the tier options with prices and stating that categories are separate entities, adding meaning 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?
The description clearly states the tool returns a quote (scope and price) for getting a business named in AI answers. It distinguishes from siblings: audit is about checking visibility, purchase is about buying, so purpose is clear.
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?
No explicit when-to-use or when-not-to-use. The 'FREE' implies it's a preliminary step, but no contrast with audit_business_visibility or purchase_engagement is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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