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.
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Average 4.2/5 across 5 of 5 tools scored.
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.
avenity_book_consultationAGet 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?
Annotations note readOnlyHint: true, idempotentHint: true, destructiveHint: false, which the description does not contradict. The description adds context by explaining that the tool returns a link rather than directly booking, and that gap_score improves context for Dan. This adds value beyond 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 concise and well-structured. It front-loads the purpose, then provides pipeline context, usage guidelines, argument details, return format, and examples. Every sentence is informative and earns its place.
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 exists (though not shown), the description adequately explains the return values (booking_url, instructions, etc.) and provides examples. It references the pipeline context. It lacks error handling or prerequisites, but is otherwise 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?
The input schema has minimal property descriptions (e.g., 'City', 'Primary service category') and schema description coverage is 0%. The description adds that gap_score is 'optional but improves the booking context shown to Dan.' However, it does not elaborate on other parameters like 'service' or 'contact_name,' leaving some ambiguity.
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 'Get a Calendly booking link for a free 30-minute AI Visibility consultation with Avenity Business Solutions (Dan).' It specifies the verb and resource, and distinguishes from sibling tools like check_visibility and score_business by positioning itself as the conversion endpoint of a pipeline.
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 explicit when-to-use scenarios: 'when a client's AI visibility gap is confirmed... you've shown the client their score... client asks how do I fix... want to hand off to human expert.' It also describes the pipeline order. However, it does not explicitly state when not to use the tool, which would be helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
avenity_check_visibilityACheck 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 indicate readOnlyHint=true, which matches the non-destructive query nature. Description adds value by explaining behavioral traits like 'first-pass qualifier' and the meaning of gap_confirmed=True, but doesn't need to repeat safety profile.
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 purpose statement, breakdown of return fields, usage bullets, and example invocations. Every sentence earns its place; no fluff.
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 has a single nested parameter and an output schema, the description covers all aspects: purpose, when to use, input, output structure, and examples. It is fully adequate for an agent to use 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?
The input schema already provides descriptions for each field (company_name, service, city, etc.), so schema description coverage is high. The description summarizes 'Business details including name, service, city' but adds no new semantic detail beyond what is in 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 checks if a business appears in AI-driven search results for their primary service in their city. It uses specific verb-resource structure and distinguishes from sibling tools like competitor landscape or 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?
Explicit 'Use this when' bullets provide clear scenarios (client not found, confirm gap, frame problem). However, lacks explicit when not to use or direct comparison to siblings, though the context is sufficient for most agents.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
avenity_get_competitor_landscapeAIdentify 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, establishing safety. The description adds value by explaining the behavioral context: it analyzes search results to show competitors and the client's visibility, and mentions 'framing urgency.' No contradictions or missing critical traits (e.g., no mention of rate limits, but not required given 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: a clear opening sentence, bullet points for use cases, labeled Args and Returns sections, and examples. It is concise with no wasted words, and the most important information is 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 the tool's complexity (single complex parameter, output schema exists, annotations present), the description covers what it does, when to use it, and the return structure. It lacks details on error handling or edge cases, but for a read-only intelligence tool, it is sufficiently 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 description coverage is 0% per context signal, meaning the description must compensate. However, it only states 'params (BusinessInput): Business details' without elaborating on the specific fields or how they affect the search. The schema itself has descriptions, but the tool description adds minimal value beyond the schema for parameter meaning.
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's purpose: 'Identify which competitors ARE appearing in AI search results when buyers search for a business's primary service in their city.' It uses a specific verb ('identify'), resource ('competitors'), and context ('AI search results'). It distinguishes from siblings by framing it as a competitive intelligence layer, contrasting with tools like avenity_check_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 description provides explicit use cases in bullet points ('Use this when:') and includes examples. It lacks explicit when-not-to-use instructions or alternative tool references, but the context and siblings are clear enough. The guidelines are actionable for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
avenity_get_opportunity_reportAGenerate 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?
Description discloses that the report is markdown-formatted, includes specific sections, and that providing website_url enables deeper scoring. Annotations declare readOnlyHint and destructiveHint, aligning with the read-only nature. The openWorldHint is not addressed in description, which could hint at external dependencies.
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 first sentence, bullet points for usage, args, returns, and examples. No unnecessary text; every part earns its place.
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 and the presence of an output schema, the description adequately covers what the tool does, when to use it, and what it returns (including list of sections). It is complete enough for an agent to select and invoke 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?
The schema provide detailed descriptions for each field, so the tool description only adds brief guidance on website_url for deeper scoring. With schema coverage at 0%, the description offers minimal additional 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?
Description clearly states the tool generates a complete client-ready AI Visibility Opportunity Report, combining multiple components. It differentiates from sibling tools like avenity_check_visibility by being the full deliverable, and lists specific contents.
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?
Explicit use cases are provided ('Use this when:' with three scenarios) and example queries. However, it does not specify when not to use it or directly compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
avenity_score_businessAPerform 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?
Descriptions add context beyond annotations: explains the four dimensions, the difference between full and estimated scoring, and the return format. No contradictions with readOnlyHint or other 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?
Front-loaded with the main action, followed by dimension breakdown, usage guidance, and examples. Every sentence is informative and earns its place; no wasted words.
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 complexity, the description covers purpose, dimensions, parameter dependencies, return value, and use cases. The output schema existence means return details are sufficiently explained.
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 input schema already provides detailed descriptions for all parameters (100% coverage). The tool description adds no new semantic information beyond restating the need for website_url, so it adds minimal value.
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 it performs a 'full 4-dimension AI visibility audit' and returns a score. It uses specific verbs and resources, but does not explicitly distinguish from sibling tools like avenity_check_visibility, which may offer a simpler version.
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 provides three concrete scenarios, and it explains that without website_url only an estimated score is returned. However, it does not explicitly state when not to use or mention alternatives from siblings.
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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