AI VisibilityGenerative AI SamplingActive in MCP

Inspect Ai Visibility

inspect_ai_visibility(domain: string, competitors?: string[], platform?: enum)

How a domain shows up in AI answers, vs named competitors.

Compares how frequently your domain is mentioned in generative AI answers compared to named competitors, including share-of-voice calculations.

Input Parameters

3 arguments
ParameterTypeRequirementDefault / ConstraintsDescription
domainstringrequired
none
Your domain to measure AI brand mentions for.
competitorsstring[]optional
Array of up to 5 competitor domains
Named competitor domains to compare against.
platformenumoptional
default: googlegoogle | chat_gpt
AI platform to inspect (google = AI Overview; chat_gpt = ChatGPT).

Return Envelope (OFE / 1.0)

Open Fact Envelope

Every response adheres to the strict ofe/1.0 envelope schema, returning verified data, typed facts, entity references, and follow-up tool suggestions:

data Payload

Compares how frequently your domain is mentioned in generative AI answers compared to named competitors, including share-of-voice calculations.

coverage & Freshness

Reports returned count vs total items, observation timestamp as_of, and scope notes. Evaluated live per request.

resources (Dataset Exports)

If a query yields high row counts (e.g. >1,000 queries in Search Console), full unpaginated tables are persisted to R2 and linked as an mcpseo:// URI for follow-up retrieval via export_dataset.

next_actions

Provides suggested follow-up tool calls with pre-filled arguments so your AI agent can navigate from discovery to detailed inspection autonomously.

Emitted Facts & Entities

Knowledge Graph

Typed Facts

Facts emitted in the facts[] array with provenance receipts:

Fact TypeClaim DescriptionEmitted Data Fields
ai_visibility.brand_mentionsHow often a domain is mentioned in AI answers, its share of voice among the domains compared, and the sources cited most.
domainis_youmentionsai_search_volumeshare_of_voicetop_cited_sources

Registered Entities

Entities registered in the entities[] array to establish subject relationships:

domain

Target domain and competitor domains.

Request & Response Examples

Live MCP Payloads

Exact JSON schemas transmitted over Model Context Protocol (stdio or HTTP SSE transport):

1. Client Tool Invocation

inspect_ai_visibility request.json
{
  "tool": "inspect_ai_visibility",
  "arguments": {
    "domain": "linear.app",
    "competitors": [
      "jira.com",
      "asana.com"
    ],
    "platform": "google"
  }
}

2. Server Envelope Response

inspect_ai_visibility response.json
{
  "schema_version": "ofe/1.0",
  "domain": "ai_visibility",
  "data": {
    "domain": "linear.app",
    "competitors": [
      "jira.com",
      "asana.com"
    ],
    "platform": "google"
  },
  "facts": [
    {
      "type": "ai_visibility.brand_mentions",
      "subject": [
        "urn:domain:linear.app"
      ],
      "data": {
        "domain": "linear.app",
        "is_you": true,
        "mentions": 42,
        "share_of_voice": 0.38
      },
      "provenance": {
        "source_class": "ai_answer",
        "method": "ai_answer_mentions",
        "confidence": 0.5,
        "observed_at": "2026-09-24T00:00:00Z",
        "cache_hit": false
      }
    },
    {
      "type": "ai_visibility.brand_mentions",
      "subject": [
        "urn:domain:jira.com"
      ],
      "data": {
        "domain": "jira.com",
        "is_you": false,
        "mentions": 51,
        "share_of_voice": 0.46
      },
      "provenance": {
        "source_class": "ai_answer",
        "method": "ai_answer_mentions",
        "confidence": 0.5,
        "observed_at": "2026-09-24T00:00:00Z",
        "cache_hit": false
      }
    }
  ],
  "entities": [
    {
      "id": "urn:domain:linear.app",
      "kind": "domain",
      "label": "linear.app"
    },
    {
      "id": "urn:domain:jira.com",
      "kind": "domain",
      "label": "jira.com"
    }
  ],
  "coverage": {
    "returned": 2,
    "total": 3,
    "as_of": "2026-09-24T00:00:00Z",
    "scope_note": null
  },
  "deltas": [],
  "resources": [],
  "next_actions": []
}

LLM Agent Workflow

Claude & Cursor Integration
Step 1

Prompt Trigger

"What is linear.app's share of voice in Google AI Overviews compared to jira.com?"
Step 2

Agent Decision & Reasoning

The agent compares multi-brand presence in generative search to quantify LLM visibility changes over time.

Step 3

Recommended Follow-up Tools

Provenance Guarantees & Error Handling

Reliability
Source Classai_answer
Inspection Methodai_answer_mentions
OFE Calibration Score0.50 (OFE 1.0 scale)
Cache DurationNone (Live)

Every fact emitted by Vouched includes source_class, method, observed_at ISO timestamp, and a published confidence score. LLM agents can inspect these citations to distinguish first-party verified facts (e.g. Search Console) from modeled competitor estimates.

Billing & API Keys

Paid market data, on Pro and Team. A call typically costs about $0.10 per domain compared of market data, so Pro's included $4 covers roughly 40 domains compared. Self-hosted, it uses your own DataForSEO key and bills you directly, with nothing added. See pricing for what each tool costs.

More AI Visibility Tools

1 sibling tools
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