Segment Visibility by model, region, or persona

GET/domains/{domainId}/topics/{topicId}/visibility/aggregated

The segment switcher on the Visibility tile — a score per segment along three axes: models via a query filter, region via the per-market topic, and persona via prompt tags.

How this example works

For a by-model split you may not need models=. modelBreakdown[] already returns per-model scores in one unfiltered call — reach for models= only for a fully filtered payload (chart, competitors, mentions scoped to that model).
Regions are segmented by topic, not by query param. Topics carry their own market + language — a multi-market brand has a sibling topic per market (List topics), so the region axis is "call the same recipe with that market's topicId". Topic visibility ignores marketLanguageCodePairId; that param filters the domain-level endpoints (sentiment/aggregated, alignment/aggregated, raw results).
There is no persona query param. Personas are modeled as prompt tags: list them with GET /tags (org-wide bare array, returns { id, name, ... }), then pass the tag id values as tagIds — they are UUIDs, OR semantics; non-UUID values are rejected with a 400.

curl "https://api.getmint.ai/api/tags" -H "X-API-Key: $MINT_API_KEY"

curl "https://api.getmint.ai/api/domains/$DOMAIN_ID/topics/$TOPIC_ID/visibility/aggregated?models=gpt-5" \
  -H "X-API-Key: $MINT_API_KEY"
curl "https://api.getmint.ai/api/domains/$DOMAIN_ID/topics/$US_TOPIC_ID/visibility/aggregated" \
  -H "X-API-Key: $MINT_API_KEY"
curl "https://api.getmint.ai/api/domains/$DOMAIN_ID/topics/$TOPIC_ID/visibility/aggregated?tagIds=7c9e6679-7425-40de-963d-1d8f0a3c2e11" \
  -H "X-API-Key: $MINT_API_KEY"

Response — GET /tags returns the tag library; each segment call returns a scoped report (trimmed; full shape in API reference):

[
  {
    "id": "7c9e6679-7425-40de-963d-1d8f0a3c2e11",
    "name": "Buyer persona",
    "organizationId": "fe0b1256-05e6-4214-81db-a5f1f88616b4",
    "createdAt": "2026-06-01T09:00:00.000Z",
    "updatedAt": "2026-06-01T09:00:00.000Z"
  }
]

The scoped report below was filtered with models=gpt-5. Note that only the headline fields (averageScore, scoreVariation) honour the filter — availableModels and modelBreakdown always describe the full catalogue with each model's own unfiltered score (verified against the live API):

{
  "averageScore": 74,
  "scoreVariation": 2.1,
  "availableModels": ["gpt-interface", "sonar-pro", "copilot-interface", "grok-4", "google-ai-overview", "gpt-5", "claude-sonnet-4", "gemini-2.5-flash", "gpt-5-mini"],
  "modelBreakdown": [
    { "model": "gpt-5", "score": 74, "variation": 2.1 },
    { "model": "claude-sonnet-4", "score": 61, "variation": -1.2 },
    { "model": "gemini-2.5-flash", "score": 48, "variation": 0 }
  ]
}

Query parameters

Prop

Type

Variations

Change	Effect
Combine `models` + `tagIds`	Each filter narrows the payload further; multiple `tagIds` match with OR semantics
Read `modelBreakdown[]`	By-model split without one filtered call per model
`marketLanguageCodePairId` on `sentiment/aggregated` or `alignment/aggregated`	Region split for domain-level perception metrics (`<marketCode>-<languageCode>`, e.g. `"US-en"`)
Pass `topicId` on a domain-level endpoint	Scope a domain-wide aggregate down to a single topic

Common follow-ups

Conventions

Base URL, auth, ISO dates, default window, and array encoding.

Visibility Score for one asset

The single-number headline this recipe splits into segments.

curl "https://api.getmint.ai/api/tags" -H "X-API-Key: $MINT_API_KEY" curl "https://api.getmint.ai/api/domains/$DOMAIN_ID/topics/$TOPIC_ID/visibility/aggregated?models=gpt-5" \ -H "X-API-Key: $MINT_API_KEY" curl "https://api.getmint.ai/api/domains/$DOMAIN_ID/topics/$US_TOPIC_ID/visibility/aggregated" \ -H "X-API-Key: $MINT_API_KEY" curl "https://api.getmint.ai/api/domains/$DOMAIN_ID/topics/$TOPIC_ID/visibility/aggregated?tagIds=7c9e6679-7425-40de-963d-1d8f0a3c2e11" \ -H "X-API-Key: $MINT_API_KEY"

[ { "id": "7c9e6679-7425-40de-963d-1d8f0a3c2e11", "name": "Buyer persona", "organizationId": "fe0b1256-05e6-4214-81db-a5f1f88616b4", "createdAt": "2026-06-01T09:00:00.000Z", "updatedAt": "2026-06-01T09:00:00.000Z" } ]

{ "averageScore": 74, "scoreVariation": 2.1, "availableModels": ["gpt-interface", "sonar-pro", "copilot-interface", "grok-4", "google-ai-overview", "gpt-5", "claude-sonnet-4", "gemini-2.5-flash", "gpt-5-mini"], "modelBreakdown": [ { "model": "gpt-5", "score": 74, "variation": 2.1 }, { "model": "claude-sonnet-4", "score": 61, "variation": -1.2 }, { "model": "gemini-2.5-flash", "score": 48, "variation": 0 } ] }

Variations

Change	Effect
Combine `models` + `tagIds`	Each filter narrows the payload further; multiple `tagIds` match with OR semantics
Read `modelBreakdown[]`	By-model split without one filtered call per model
`marketLanguageCodePairId` on `sentiment/aggregated` or `alignment/aggregated`	Region split for domain-level perception metrics (`<marketCode>-<languageCode>`, e.g. `"US-en"`)
Pass `topicId` on a domain-level endpoint	Scope a domain-wide aggregate down to a single topic