A
licenseA
qualityD
maintenanceAudit your brand's visibility across ChatGPT, Perplexity, Claude, and Google AI - get citation rates, AEO health scores, content gap analysis, and a 9-page content suite to rank in AI-generated answers.
5
84
MIT
Specularis AI Visibility Audit
Server Details
Free AI visibility (GEO/AEO) audit: can ChatGPT, Claude and Perplexity find and cite your website?
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- AdevAarons/specularis-mcp-audit
- GitHub Stars
- 0
- Server Listing
- Specularis AI Visibility Audit
Available Tools
3 toolsbook_strategy_callBook a Strategy CallARead-onlyInspect
Get the link to book a free Specularis strategy call about AI visibility / GEO / AEO.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| booking_url | Yes | Link to book a free 15-minute Specularis strategy call. |
TDQS
A3.7/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, so the agent already knows this is a safe, non-destructive read operation. The description confirms the action is simply retrieving a booking link, adding the detail that the call is free and topic-specific. No contradictions found. The description adds modest value beyond annotations but doesn't describe any return format details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that describes the action, the cost (free), and the topic. Zero waste, every word earns its place. Ideal conciseness for a zero-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with readOnlyHint=true and an output schema present, the description is essentially complete. It tells the user what they get (booking link), the subject matter, and that it's free. The only minor gap is not naming the sibling alternative explicitly, but the context signals show a clear distinction.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters, the schema carries no semantic burden. The description explains what the tool returns (a link to book a free strategy call) and the topic, which is the entire semantic meaning needed. Since there are no params to document, and the description covers what the user gets, this is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear purpose: get the link to book a free strategy call about AI visibility/GEO/AEO. It specifies a concrete verb+resource ('Get the link to book') and describes the topic. It doesn't explicitly differentiate from siblings, but the sibling (run_ai_visibility_audit) is a distinctly different action, so this description is unambiguous about its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives context about what the call covers (AI visibility / GEO / AEO) and that it's free, implying when a user would want this. However, it does not explicitly state when to use this versus run_ai_visibility_audit, nor any exclusions or prerequisites. The when-to-use is implied by topic match rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_ai_citationsFind AI Citation SourcesARead-onlyInspect
Given a buyer query (e.g. 'best real estate agent in Tampa') and a website domain, find the exact sources ChatGPT, Perplexity, Claude, and Gemini cite when answering that query — and whether the domain appears in any of them. Returns the ranked source list (with which engine cites each) and an 'appears in X of N' gap. Use this whenever a user wants to know where AI gets its answers about their industry, which pages AI trusts for a query, or whether their business shows up in AI recommendations.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The question a customer would ask AI, e.g. 'best personal injury lawyer in Miami'. | |
| domain | Yes | The website to check for, e.g. example.com |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| query | Yes | |
| domain | Yes | |
| sources | Yes | |
| appears_in | Yes | How many of those sources the domain currently appears in. |
| booking_url | Yes | |
| total_sources | Yes | How many distinct sources AI cites for this query. |
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only and non-destructive; the description adds the behavioral outcome: which engines are searched, a ranked source list, and an 'appears in X of N' gap. No contradiction with 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences front-load the core purpose and return shape, then give usage triggers. Every clause earns its place; there is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple two-parameter schema, read-only annotations, and a rich output schema, the description covers inputs, engines, output semantics, and when to use it. Nothing needed for correct invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents query and domain. The description adds buyer-query context and concrete examples ('best real estate agent in Tampa', 'example.com') that help an agent map user intent to the two parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a concrete verb-object pair: 'find the exact sources ChatGPT, Perplexity, Claude, and Gemini cite' for a given query and domain, then specifies the returned ranked source list and domain-presence gap. This makes the tool's function unmistakable and clearly distinct from the audit/booking siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit triggers: 'Use this whenever a user wants to know where AI gets its answers about their industry, which pages AI trusts for a query, or whether their business shows up in AI recommendations.' It does not name alternatives or when-not-to-use, but the invocation context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_ai_visibility_auditRun AI Visibility AuditAInspect
Run a free AI visibility (GEO/AEO) audit on a website — checks whether ChatGPT, Claude, Perplexity, and Gemini can find and cite it. Returns an instant snapshot of crawler access, structured data, and llms.txt. If an email is provided, a full scored report (0–100 across 5 pillars, with copy-paste fixes) is emailed as a PDF. Use this whenever a user asks to audit/check a site's AI visibility, GEO, AEO, or whether AI can find them.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Optional name for the report greeting. | |
| role | No | Optional. Tailors the audit lens — local-service providers are scored on local entity signals, reviews, and directories. | |
| No | Optional. If provided, the full scored PDF report is emailed here (and the user becomes a Specularis lead). Omit for just the instant snapshot. | ||
| website_url | Yes | The website to audit, e.g. https://example.com |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| website | Yes | The normalized website that was audited. |
| llms_txt | Yes | Whether an llms.txt file is present. |
| booking_url | Yes | Link to book a Specularis strategy call. |
| report_email | No | The email the full report was sent to, if requested. |
| structured_data | Yes | Summary of JSON-LD structured data found on the homepage. |
| ai_crawler_access | Yes | Whether major AI crawlers (GPTBot, ClaudeBot, PerplexityBot) can access the site. |
| full_report_status | Yes | Status of the full scored PDF report. |
TDQS
A4.1/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations show readOnlyHint=false and openWorldHint=true, and the description honestly discloses the nontrivial side effect that providing an email 'becomes a Specularis lead' and triggers a PDF email. It also clarifies that omitting email yields only the instant snapshot, so the agent understands both behavioral paths. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: definition, conditional output behavior, and usage trigger. The most important scoping information is front-loaded in the first sentence. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 params, an output schema, and meaningful optional behavior, the description covers the audit scope, snapshot contents, scored report, email side effect, and when to invoke it. It doesn't mention sibling routing or timing expectations, but those are covered by annotations and schema context and are not essential to a correct call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already documents all four parameters with 100% coverage, so baseline is 3. The description adds meaning beyond the schema by explaining the behavioral consequence of the email parameter (lead creation + emailed PDF) and confirming that website_url is the audit target. It does not add new detail for name/role, but the schema covers those adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and object — 'Run a free AI visibility (GEO/AEO) audit on a website' — and defines the tool's scope by naming the AI engines it checks (ChatGPT, Claude, Perplexity, Gemini). It is unambiguous about what the tool does, but it does not explicitly differentiate from siblings like find_ai_citations or book_strategy_call. That prevents a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides an explicit trigger condition: 'Use this whenever a user asks to audit/check a site's AI visibility, GEO, AEO, or whether AI can find them.' It also clarifies the conditional path for email vs. omit-email. However, it does not state when to prefer a sibling tool or what counts as a non-audit request, so it stops short of full when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
- Added
find_ai_citations
2 tool updates
- Changed
book_strategy_call2 fields changed- removed
Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#" - changed
Output schema / (root)Previous value: -nullNew value: +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "additionalProperties": false, + "properties": { + "booking_url": { + "description": "Link to book a free 15-minute Specularis strategy call.", + "type": "string" + } + }, + "required": [ + "booking_url" + ], + "type": "object" +}
- Changed
run_ai_visibility_audit1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "additionalProperties": false, + "properties": { + "ai_crawler_access": { + "description": "Whether major AI crawlers (GPTBot, ClaudeBot, PerplexityBot) can access the site.", + "type": "string" + }, + "booking_url": { + "description": "Link to book a Specularis strategy call.", + "type": "string" + }, + "full_report_status": { + "description": "Status of the full scored PDF report.", + "enum": [ + "sent", + "failed", + "not_requested" + ], + "type": "string" + }, + "llms_txt": { + "description": "Whether an llms.txt file is present.", + "type": "string" + }, + "report_email": { + "description": "The email the full report was sent to, if requested.", + "type": "string" + }, + "structured_data": { + "description": "Summary of JSON-LD structured data found on the homepage.", + "type": "string" + }, + "website": { + "description": "The normalized website that was audited.", + "type": "string" + } + }, + "required": [ + "website", + "ai_crawler_access", + "structured_data", + "llms_txt", + "full_report_status", + "booking_url" + ], + "type": "object" +}
2 tool updates
- First observed
book_strategy_call - First observed
run_ai_visibility_audit
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TDQS
A4.2/5.0
Disambiguation4/5
The booking tool is clearly separate. The two audit-related tools overlap conceptually (both deal with AI citation/visibility), but find_ai_citations is tied to a specific buyer query and returns per-engine source rankings, while run_ai_visibility_audit evaluates a site's technical visibility. Descriptions mostly make this distinction usable, though an agent could still hesitate on ambiguous 'why don't I show up' requests.
Naming Consistency5/5
All three tools follow a consistent verb_noun snake_case pattern: book_strategy_call, find_ai_citations, run_ai_visibility_audit. The verbs clearly reflect the action and the nouns refer to the key object, making the set predictable and easy to navigate.
Tool Count5/5
Three tools is a reasonable, minimal footprint for a focused audit-and-call service. Each tool earns its place: discover sources, run an audit, and book a strategy call. No redundant or filler tools are present.
Completeness5/5
For the stated domain, the set covers the core user journeys: checking where AI answers come from for a query, running a full website-level visibility audit, and starting a commercial relationship with a call. Audit results can be delivered on-screen or by email, and fixes are included in the emailed report. No critical workflow dead-end is apparent.