Checks if AI assistants name a local business. Free shareable report, honest fixes, no guarantees.
Score how ready a local business is to be named when people ask AI engines (ChatGPT, Gemini, Google AI) for a recommendation in its category, and get a free, shareable Radveo report with the exact fixes to improve its odds. Scores readiness from the business's own site; it does not query an engine live. Honest answer-engine optimization: improves the odds of being cited, never guarantees a placement.
| Name | Required | Description | Default |
|---|---|---|---|
| website | No | The business website URL, e.g. https://joesplumbing.com. Optional if business_name is given; improves accuracy. | |
| location | No | City or region, e.g. "Seattle, WA". Optional but improves accuracy. | |
| business_name | No | The business name, e.g. "Joe's Plumbing". Provide this OR a website. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | False when the check could not be completed (bad input, rate limit, or the audit engine timed out). |
| note | Yes | Honesty framing: an indicative, measured read of how AI assistants tend to answer, never a guarantee of placement. |
| verdict | Yes | Whether AI engines named this business when asked customer-style recommendation questions. 'named' = at least one query named it; 'absent' = confidently not named; 'unknown' = the engines could not give a confident read; 'not_checked' = the live AI-answer probe did not run for this call (the on-page audit still did). |
| business | No | The business name or website that was checked. |
| seoGrade | No | Letter grade (A-D) for the readiness score, when measured. |
| seoScore | No | 0-100 on-page AI-visibility readiness score, when measured. |
| topFixes | No | The top fixes to improve the odds of being named (max 3 here; full list in the report). |
| reportUrl | Yes | Free, shareable, public report URL for this check. |
| enginesChecked | No | The AI engine panel the probes consult: an indicative read of how AI assistants tend to answer, not a live probe of every public engine. |
| queriesChecked | No | The customer-style questions asked to the AI engines (empty when the probe didn't run). |
| competitorsNamed | No | Businesses surfaced instead of this one (deduped). Same list the public shareable report shows. |
| aiVisibilityScore | No | 0-100: the share of customer-style AI queries that named the business. Null when the probe didn't produce a measured score. |
| competitorsSource | No | 'ai' = an AI engine named them; 'places' = real local leaders from Google's local results (shown when the engines named none). Null when no competitors surfaced. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does meaningful work: it explicitly states the tool does not query an engine live, scores from the business's own site, and never guarantees placement. This discloses key behavioral limits and expectations beyond a generic 'assess' definition.
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 front-loaded with the core purpose, then adds the most important behavioral caveat, then sets expectations about guarantees. Every sentence earns its place and there is no redundant or filler wording.
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 tool with an output schema and fully described optional parameters, the description covers the essential decision context: what it measures, how it measures it, what it returns, and its limits. The only real gap is not addressing the sibling tool or providing explicit selection guidance, which is already captured under usage guidelines.
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 parameter schema already documents website, location, and business_name including optionality and accuracy trade-offs. The description adds context about scoring from the business's own site but does not materially change parameter understanding.
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 names a specific action ('Score how ready a local business is'), a specific resource ('AI engines... recommendation'), and a concrete deliverable ('free, shareable Radveo report with exact fixes'). It also distinguishes itself from a generic guide by clarifying it evaluates readiness from the business's own site rather than querying an engine live.
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 when to use it (when a local business wants to know its AI-citation readiness) but does not explicitly address when not to use it or how it differs from the sibling tool 'how_to_appear_in_ai_search'. No alternative or exclusion is mentioned, leaving the agent to infer routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Radveo's honest, practical playbook for getting a local business found and cited inside AI engine answers (ChatGPT, Gemini, Google AI Overviews). Answer-engine optimization (AEO) steps any owner can act on.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| note | Yes | Honesty framing: these steps improve odds, they never guarantee placement. |
| steps | Yes | The AEO steps, in order. Empty only when the server is rate-limiting. |
| freeCheckUrl | Yes | Where to run the free AI-visibility check. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The verb 'Get' implies a read-only, non-destructive operation, which is a positive signal. However, the description does not explicitly state whether the tool has any side effects, requires authentication, or has rate limits. It also does not mention the format of the returned playbook, though an output schema exists. The disclosure is adequate but not comprehensive.
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 a single, well-structured sentence that fronts the key information: what the tool provides and for whom. It avoids unnecessary detail and is immediately actionable. Every clause contributes to understanding the tool's purpose, making it concise and effectively structured.
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 zero-parameter tool with an output schema, the description is complete. It clearly states what the tool returns (a playbook with AEO steps), the target audience (local business owners), and the scope (AI engine answers). Since an output schema exists, the description does not need to explain return values. The tool is simple and the description covers all necessary context for an agent to invoke 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?
The tool has zero parameters, and the input schema is an empty object. The schema description coverage is trivially 100%, and with no parameters to document, the description does not need to add parameter semantics. According to the baseline rule for 0 params, a score of 4 is appropriate; no additional parameter information is needed.
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: 'Get Radveo's honest, practical playbook for getting a local business found and cited inside AI engine answers.' It uses a specific verb ('Get') and a specific resource ('playbook'), and it names the target AI engines. This distinguishes it from the sibling tool 'check_ai_visibility' which presumably checks visibility rather than providing a how-to guide.
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 no explicit guidance on when to use this tool versus the sibling 'check_ai_visibility'. It does not mention any alternatives or exclusion criteria. The only implied usage is 'if you want the playbook, use this', but there is no direct comparison or context to help an agent choose between the two tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
check_ai_visibility1 field changedOutput schema / properties / note / descriptionPrevious value: -"Honesty framing: results improve odds and show what AI says today, never a guarantee of placement."New value: +"Honesty framing: an indicative, measured read of how AI assistants tend to answer, never a guarantee of placement."check_ai_visibility2 fields changedOutput schema / properties / enginesChecked / descriptionPrevious value: -"The AI engine panel the probes consult — an indicative read of how AI assistants tend to answer, not a live probe of every public engine."New value: +"The AI engine panel the probes consult: an indicative read of how AI assistants tend to answer, not a live probe of every public engine."Output schema / properties / note / descriptionPrevious value: -"Honesty framing: results improve odds and show what AI says today — never a guarantee of placement."New value: +"Honesty framing: results improve odds and show what AI says today, never a guarantee of placement."check_ai_visibilityhow_to_appear_in_ai_searchClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
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Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
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Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
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See whether AI assistants recommend your business - and where you rank - without leaving the chat.
Free AI visibility check: is your business cited when customers ask AI? Score plus competitors.
AI visibility + fact-checks (ChatGPT/Perplexity), review gaps & competitor scans, local SEO.
Monitor brand visibility, citations, competitors, sentiment, and GEO performance in AI search.
Add one secure layer between your agents and this server.
The two tools have distinct purposes: one provides a diagnostic score/report, the other offers an instructional playbook. They are clearly separate in function, though both relate to AI visibility. There is minimal risk of one being chosen when the other is intended.
Both tool names use lowercase with underscores and are descriptive. 'check_ai_visibility' follows a verb_noun pattern, while 'how_to_appear_in_ai_search' is more of a phrase but still consistent in style and readability. The deviation is minor.
With only two tools, the server feels thin for a domain like answer-engine optimization. While the two tools cover diagnosis and guidance, a more complete server might include additional tools such as generating reports or managing leads. The count is borderline but acceptable for a narrow niche.
The server covers two key aspects: assessing readiness and providing actionable steps. However, there are notable gaps—no tool for tracking progress, comparing competitors, or handling specific business categories. The surface is minimal and leaves room for expansion.
