Avenity AI Visibility
Server Details
Hireable AI-visibility service: get a business named in AI answers. Free audit + x402 hire.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
Average 4.3/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: audit checks visibility, request provides a quote, and purchase executes the engagement. No overlap.
All names follow a consistent verb_noun pattern with snake_case (e.g., audit_business_visibility, purchase_engagement).
Three tools perfectly cover the core workflow for an AI visibility service: diagnose, quote, purchase. Not too many or too few.
The tool surface covers the complete sales funnel for visibility services, with no obvious gaps for the stated purpose.
Available Tools
3 toolsaudit_business_visibilityAInspect
FREE. 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"). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the tool is free and implies a read-only check, but lacks specifics on behavior (e.g., what if not found, return format, rate limits). The presence of an output schema mitigates some concerns, but description alone is moderate.
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, zero wasted words. First sentence states purpose; second adds functional context. Front-loaded and efficient.
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 presence of an output schema (not shown but indicated), the description does not need to detail return values. It covers core purpose, usage context, and parameters. Missing error handling or prerequisites, but still fairly complete for a simple diagnostic tool.
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% with good parameter descriptions. The description reinforces the schema by providing slightly richer context (e.g., 'buyer category / product line'), adding marginal value. Baseline 3 is appropriate.
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') and resource ('whether a business is named in AI answers') and clearly distinguishes the tool as a top-of-funnel diagnostic, differentiating it from sibling tools like purchase_engagement which likely involve next steps.
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 explicitly states this is a top-of-funnel tool for diagnosing a client, giving clear context for when to use it. While it does not explicitly list alternatives or exclusions, the usage context is strong enough for an AI agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
purchase_engagementAInspect
Hire 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. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description fully discloses the gated behavior (x402), payment requirements, settlement, recording, and confirmation. It details the two possible outcomes based on payment_proof, leaving no ambiguity about the tool's effects.
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, two sentences, and front-loaded with the core action and gating. Every sentence adds essential information without redundancy.
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 (5 params, gated flow, output schema), the description covers the key behavioral aspects, references the sibling for quotes, and doesn't need to detail return values due to the output schema. It is complete for effective use.
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%, so baseline is 3. The description adds value by linking 'tier' to request_engagement_quote and explaining 'payment_proof' as an x402 token, which goes beyond the schema's description.
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 'Hire Avenity' as the action, specifying the resource and the gated nature with payment_proof. It distinguishes from siblings by referencing request_engagement_quote for pricing and audit_business_visibility for a different 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 description explains the two scenarios (with/without payment_proof) and references request_engagement_quote for tier information, providing clear context on when to use. However, it doesn't explicitly state when not to use or list all alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_engagement_quoteAInspect
FREE. 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 |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It mentions 'FREE' indicating no cost to call, but does not explicitly state if the tool is read-only or has side effects. It lacks details on auth or rate limits, but the core behavior (returning scope and price) is clear.
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 very concise: two sentences with critical information front-loaded. No superfluous text. Every line serves a purpose.
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 presence of an output schema (not shown), the description adequately explains the tool's purpose and key parameters. Minor gaps exist, such as constraints on business_name or error cases, but overall sufficient for a simple quote tool.
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 0%, but the description adds significant value by explaining categories as separate entities and enumerating tier options with their costs. This compensates fully for the lack of schema documentation.
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 the scope and price for getting a business named in AI answers for given categories. It distinguishes from siblings 'audit_business_visibility' (checks visibility) and 'purchase_engagement' (buys engagement) by focusing on quoting.
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 context on when to use (to get a quote before purchasing) and includes pricing tiers. However, it does not explicitly state when not to use or provide exclusions, but the context is clear enough for an agent to infer.
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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