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apiacre.com Data Profiler
x402
apiacre.com Data Profiler is a paid API for AI agents from apiacre.com, paid per call via x402, $0.025/call, status unknown (last checked 2026-09-13).
Analyzes a dataset to return field-level statistics including null counts, inferred types, uniqueness ratios, and sample values.
Price / call$0.025
Success—
StatusUnknown
How this service works
Profile CSV, TSV, JSON, JSON Lines, or YAML records for field types, nulls, uniqueness, and representative samples.
Output
Returns a per-field breakdown including: field name, inferred data type, null count and null rate, unique value count and uniqueness ratio, and a set of sample values drawn from the column.
Example use cases
Auditing data quality before ETL pipeline
“Before I load this JSON dataset into our warehouse, can you profile it and tell me which fields have nulls, what types they are, and how unique the values are? Here's the records array.”
Exploring an unfamiliar dataset
“I just received this CSV converted to JSON and I have no documentation on it — can you profile the fields so I can understand what types of data are in each column and see a few sample values?”
Detecting dirty data before ML training
“I want to check this training dataset for data quality issues — can you run a field profile on it and show me null rates, uniqueness scores, and inferred types for every column?”
Technical details
Endpoint
POSThttps://apiacre.com/v1/data/profileWhen to prefer
Use this endpoint when you need a fast, automated structural summary of a dataset before loading, transforming, or modeling it. It is ideal for data quality audits, exploratory data analysis, and pre-pipeline inspection where you need null rates, type inference, uniqueness, and samples in one call — without writing custom profiling code.
Side effects
- Empty or malformed input records return a validation error
- Records with deeply nested structures may not profile nested fields
- Very large datasets may time out or require batching
- Inconsistent types within a field may result in ambiguous or mixed type inference
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| format | string | |
| content | string |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"rows": 2,
"columns": 2,
"profile": {
"name": {
"null": 0,
"types": {
"str": 2
},
"unique": 2,
"nonNull": 2,
"samples": [
"Ada",
"Lin"
]
},
"score": {
"null": 0,
"types": {
"str": 2
},
"unique": 2,
"nonNull": 2,
"samples": [
"98",
"91"
]
}
}
},
"meta": {
"cached": false,
"sources": [],
"warnings": [],
"duration_ms": 42,
"next_actions": [
{
"path": "/v1/data/clean",
"title": "Dataset cleaning",
"method": "POST",
"reason": "Apply collision-safe header, whitespace, and empty-value normalization after ...",
"service": "data.clean"
}
]
},
"service": "data.profile",
"version": "1",
"request_id": "018f1f54-7f38-7ba2-8dc3-5f90272d9f1a"
}
}Tags
x402
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