Check how well any business appears in AI-powered search (ChatGPT, Perplexity, Gemini) and local results. Returns an Authority Score across 8 pillars: entity clarity, schema, reviews, content depth, citations, GBP, social proof, and AI inclusion. Built for marketing agencies and AI assistants helping SMBs improve local search visibility.
Get a Calendly booking link for a free 30-minute AI Visibility consultation with Avenity Business Solutions (Dan).
The consultation is for business owners who have a confirmed AI visibility gap and want to understand the fix and investment required.
This tool is the conversion endpoint of the Avenity agent pipeline: check_visibility → score_business → get_opportunity_report → book_consultation
Use this when:
A client's AI visibility gap is confirmed and they want to act
You've shown the client their score and they're interested
A client asks "how do I fix my AI visibility?"
You want to hand off to a human expert for the close
Args: params (ConsultationInput): Client details. gap_score is optional but improves the booking context shown to Dan.
Returns: str: JSON with: booking_url (str): Direct Calendly link instructions (str): What to tell the client context_for_call (str): What Dan will know before the call audit_url (str): Self-service audit tool URL
Examples: - "Book a consultation for my client who has a 38/100 visibility score" - "How does [company] get help fixing their AI visibility?" - "Connect my client with Avenity"
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description does not need to restate safety. It adds useful behavioral context beyond annotations by describing the returned JSON fields, what Dan will know before the call, and that this is the conversion/handoff endpoint. There is no contradiction with the read-only annotation since the tool returns a booking link rather than directly creating a booking.
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-organized with clear sections for purpose, usage, arguments, returns, and examples. It is somewhat long, but most sentences add actionable guidance, such as when to use the tool and what the return payload contains.
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 return format is described in detail, the prerequisite condition (confirmed AI visibility gap) is stated, and the pipeline position gives surrounding context. The description is complete enough for an agent to select and invoke the tool, though it could add explicit error or edge-case handling.
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 description adds one meaningful detail: gap_score is optional but improves the booking context shown to Dan. However, it does not explain the required fields beyond calling them 'Client details'; the schema's nested property descriptions carry most of the parameter semantics. With schema description coverage reported as 0%, the description only partially compensates.
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: 'Get a Calendly booking link for a free 30-minute AI Visibility consultation.' It also places the tool in the pipeline 'check_visibility → score_business → get_opportunity_report → book_consultation', which clearly distinguishes it from the 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?
The 'Use this when' section gives explicit triggering conditions, such as a confirmed AI visibility gap, an interested client, or a handoff to a human expert. It does not explicitly state when not to use the tool or compare against sibling tools, but the pipeline context makes the appropriate use clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check whether a business appears in AI-driven search results when buyers search for their primary service in their city.
This is the first-pass qualifier. If gap_confirmed=True, the business is NOT appearing when buyers search — meaning they are losing customers to competitors right now. This is the core hook for Avenity's service.
Use this when:
A business client asks why they're not getting found online
You need to confirm an AI visibility gap before recommending action
You want to frame the problem before showing a full report
Args: params (BusinessInput): Business details including name, service, city.
Returns: str: JSON with: gap_confirmed (bool): True = business is NOT visible for this query appears_in_ai_results (bool): Whether company name was detected query (str): The exact buyer search query analyzed competitors_appearing (list[str]): Who IS showing up instead result_count_checked (int): Number of results analyzed recommendation (str): Next suggested action for this business
Examples: - "Check if Apex HVAC shows up when people search for HVAC in Houston" - "Is my client visible in AI search?" → call with their company details - "Find out if [company] has an AI visibility gap"
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
Identify which competitors ARE appearing in AI search results when buyers search for a business's primary service in their city.
This is the competitive intelligence layer — it shows a client exactly who is capturing their customers. Highly effective for framing urgency.
Use this when:
A client wants to know who is outcompeting them in AI search
You want to personalize the pitch with specific competitor names
Building context for an opportunity report or outreach message
Args: params (BusinessInput): Business details.
Returns: str: JSON with: query (str): The buyer search query analyzed competitors (list[dict]): Companies appearing, with name and URL your_company_visible (bool): Whether client's company appears gap_summary (str): Plain-English summary of the competitive gap
Examples: - "Who is showing up instead of my client in Houston HVAC searches?" - "Which competitors appear in AI Overview for roofing in Dallas?"
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the return format (JSON with query, competitors, your_company_visible, gap_summary) and mentions it performs an analysis of AI search results. It does not disclose rate limits or error behaviors, but the core operation is transparent. No contradiction with 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 with a purpose statement, usage bullets, Args, Returns, and Examples. The purpose is front-loaded, and every section adds unique value. It is concise enough (about 150 words) while covering essential aspects, making it easy for an agent to scan.
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?
With a single parameter (nested object), an output schema (which defines the return structure), annotations covering safety, and a description that includes examples and return format, the tool is fully specified. An agent can determine how to call it, what to expect, and when to use it. No critical missing information.
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% for the top-level 'params' parameter, yet the description only says 'params (BusinessInput): Business details.' It does not enumerate the required fields (company_name, service, city) or explain how to fill them. Although the inner schema has property descriptions, the description fails to guide the agent on constructing the input, which is essential given the low coverage metric. The description should have compensated by listing the required fields or pointing to 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 statement 'Identify which competitors ARE appearing in AI search results when buyers search for a business's primary service in their city' names a specific verb, resource (competitors in AI search), and scope (primary service and city). This clearly distinguishes it from siblings like avenity_check_visibility (which likely checks the business's own presence) and avenity_score_business (scoring). The examples reinforce the purpose without ambiguity.
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?
A dedicated 'Use this when' section lists three explicit conditions: when clients want to know who outcompetes them, when personalizing with competitor names, and when building context for reports or outreach. This provides clear when-to-use guidance relative to the other tools, though it doesn't explicitly say when not to use, the conditions are sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a complete, client-ready AI Visibility Opportunity Report for a business. Combines gap confirmation, competitor landscape, dimensional scoring, and revenue impact into a single structured output.
This is the full deliverable — ready to share with a client or use as the basis for a consulting recommendation. It shows the business exactly what they're missing, who's capturing their customers, and what it's worth.
Use this when:
A client needs a full picture before making a decision
You want a single comprehensive output to present
Preparing for a consulting call or proposal
Args: params (BusinessInput): Business details. Include website_url for full dimensional scoring.
Returns: str: Markdown-formatted report including: - AI visibility gap confirmation with buyer query - Competitors appearing in AI search - 4-dimension score breakdown - Estimated monthly revenue at risk - Specific recommendations ranked by impact - Link to book consultation with Avenity
Examples: - "Generate a full AI visibility report for my client" - "What's the complete picture for [company]?" - "Prepare an opportunity report I can show to [client]"
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool readOnly and non-destructive, and the description adds behavior beyond that: it returns a Markdown-formatted report, lists the exact sections, and notes the tool is client-ready. It also discloses that website_url enables full dimensional scoring. No contradiction with annotations exists.
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 longer than minimal, but well structured with purpose, use-when, args, returns, and examples. It front-loads the core purpose and uses bullets instead of prose. A few sentences, such as 'It shows the business exactly what they're missing,' are mildly redundant but do not undermine usability.
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 moderately complex tool with a nested input and an output schema, the description covers what the tool returns, when to use it, and one important input nuance. The presence of an output schema and readOnly annotations reduces the need to document return values or safety in greater depth.
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 only direct parameter guidance is 'Business details' plus the useful hint that website_url is needed for full dimensional scoring. The nested BusinessInput schema carries most of the field-level detail; while this hint adds value, the description does not meaningfully compensate for the 0% top-level schema description coverage of the params property.
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: 'Generate a complete, client-ready AI Visibility Opportunity Report.' It enumerates the report's components (gap confirmation, competitor landscape, dimensional scoring, revenue impact) and explicitly contrasts it with more targeted sibling tools by calling it 'the full deliverable.' An agent can distinguish this from avenity_check_visibility, avenity_get_competitor_landscape, and avenity_score_business.
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?
A dedicated 'Use this when' section gives concrete triggers: a client needs the full picture, a single comprehensive output is wanted, or a consulting call/proposal is being prepared. It stops short of naming the sibling tools it should not be used for or stating when a narrower tool is preferable, so it misses the explicit when-not/alternatives that would earn a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform a full 4-dimension AI visibility audit of a business's website and return a score from 0–100.
The four dimensions assessed:
Entity Clarity (25 pts): How clearly the website defines who they are, what they do, and where — the foundation AI engines use to understand the business.
Schema & Structure (25 pts): Technical markup (JSON-LD, LocalBusiness schema) that allows AI engines to directly read and cite business data.
Authority & Reviews (25 pts): Publicly visible social proof signals that AI engines use to assess trustworthiness.
Content Depth (25 pts): FAQ content, service pages, and text richness that enables AI engines to generate answers using the business as a source.
Requires website_url in params for full scoring. Without it, returns an estimated score based on search data only.
Use this when:
You need a detailed breakdown to explain the problem to a client
You want to identify the single highest-impact fix
Preparing a client proposal or consulting recommendation
Args: params (BusinessInput): Must include website_url for full scoring.
Returns: str: JSON with total score, label, per-dimension breakdown, and notes.
Examples: - "Score Apex HVAC's AI visibility" (with website provided) - "What's the biggest AI visibility gap for my client?" - "Get a detailed audit of [website]"
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the annotations by detailing scoring methodology, the 25-point breakdown per dimension, the fallback to an estimated score when website_url is absent, and the JSON return structure. Annotations already mark readOnlyHint=true and destructiveHint=false, and the description is consistent with that safe read-only 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?
The description is well-structured with a clear opening, a scannable bullet list of dimensions, a 'Use this when' section, and concrete examples. Every section earns its place, and the key behavioral conditions are front-loaded before the lower-level details.
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 reasonably complex audit tool, the description covers inputs, outputs, scoring dimensions, fallback behavior, and example invocations. It even describes the JSON return contents. It is slightly incomplete only in not routing the agent to sibling tools when a simpler visibility check is more appropriate, and in not explaining the required non-website parameters.
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%, and the description only highlights website_url, which is the optional field, while omitting any mention of the required company_name, service, and city fields. It does add important conditional meaning for website_url, but the burden of explaining the other parameters falls entirely on the schema. This is adequate but not comprehensive.
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: 'Perform a full 4-dimension AI visibility audit of a business's website and return a score from 0–100.' It clearly lists the four assessed dimensions, so an agent knows exactly what the tool produces. However, it does not explicitly distinguish itself from the sibling avenity_check_visibility, which appears to overlap in 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 gives explicit use cases: 'detailed breakdown to explain the problem to a client,' 'identify the single highest-impact fix,' and 'client proposal or consulting recommendation.' It also explains the conditional behavior based on presence of website_url. It lacks explicit when-not-to-use guidance or alternative tool routing, so it does not earn a 5.
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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Each tool has a distinct role in the consultation pipeline: visibility check, scoring, competitor analysis, report generation, and booking. No overlap, and descriptions clearly differentiate them.
All tools follow the consistent pattern 'avenity_<verb>_<noun>' in snake_case, making them predictable and easy to understand.
With 5 tools, the set is tightly scoped to the AI visibility consultation domain. Each tool is justified and contributes to a complete workflow without bloat.
The pipeline is fully covered: from initial check to scoring, competitive landscape, opportunity report, and booking. No obvious gaps for the intended purpose.