Get aggregated alignment data for a domain
X-API-Key<token>API Key for programmatic access
domainId*stringstartDate?stringStart date for the report range (ISO 8601). On aggregated endpoints, omit to default to the last 6 months (there is no "all time"); an explicit start is honoured at any length.
endDate?stringEnd date for the report range (ISO 8601). Defaults to now when omitted.
models?array<string>Filter by specific models
topicId?stringFilter by specific topic ID
promptId?stringFilter raw results to a single prompt ID. Used by the sentiment heatmap drawer to lazy-load one cell's LLM response instead of bundling all responses in the aggregate.
includeVariation?booleanInclude variation calculation comparing to previous period
truepage?numberPage number for paginated responses
1 <= value1limit?numberItems per page for paginated responses
1 <= value <= 10050marketLanguageCodePairId?stringFilter by specific market/language pair ID
tagIds?array<string>Filter by tag IDs (visibility_result joined with prompt_tag)
Aggregated alignment data with scores over time
application/json- response
averageScore*numberAverage alignment score across all selected reports and models
scoreVariation*numberScore variation percentage compared to previous period
availableModels*array<string>List of available models in the data
modelBreakdown*array<>Per-model alignment scores. Each scan produces one report per model; a model is scored over the reports it appears in.
chartData*array<>Time series data for charts
aggregatedAttributeScores*Aggregated scores keyed by attribute id; names are in attributeBreakdown
attributeBreakdown*array<>One entry per attribute: current ones first, then removed ones, each by name
reportCount*numberNumber of reports included in aggregation
dateRange*Date range of included reports
citations?Aggregated citations from all reports
totalAnswers*numberTotal number of LLM answers across all reports
modelAttributeScores?Attribute scores aggregated by model. Keys are model names, values map attribute id to score.
curl -X GET 'https://api.getmint.ai/api/domains/string/alignment/aggregated'{ "averageScore": 0.78, "scoreVariation": 3.2, "availableModels": [ "chatgpt", "claude" ], "modelBreakdown": [ { "model": "chatgpt", "score": 0.78, "variation": 0 } ], "chartData": [ { "date": "2026-05-25T10:30:00.000Z", "score": 0.78, "reportId": "3fa85f64-5717-4562-b3fc-2c963f66afa6" } ], "aggregatedAttributeScores": { "3fa85f64-5717-4562-b3fc-2c963f66afa6": 0.82 }, "attributeBreakdown": [ { "attributeId": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "attribute": "Innovation", "removed": false, "score": 0.82, "variation": 4.5 } ], "reportCount": 12, "dateRange": { "start": "2026-04-25T00:00:00.000Z", "end": "2026-05-25T10:30:00.000Z" }, "citations": { "items": [ { "domain": "techcrunch.com", "url": "https://techcrunch.com/2026/01/10/footwear-trends", "title": "Footwear Trends 2026", "prompts": [ "What are the best running shoes?" ], "sentiments": [ "positive", "neutral" ], "scores": [ 0.82, 0.74 ], "count": 12, "models": [ "chatgpt", "claude" ], "text": "Nike continues to lead in athletic footwear innovation." } ], "uniqueDomains": 86, "totalCitations": 512 }, "totalAnswers": 480, "modelAttributeScores": { "chatgpt": { "3fa85f64-5717-4562-b3fc-2c963f66afa6": 0.85 } }}