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CORE CSV Analyzer
x402
CORE CSV Analyzer is a paid API for AI agents from api.coregroupimmo.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-13).
Validates a raw CSV payload for data quality issues including missing cells, duplicate rows, missing required columns, and type mismatches before import.
Price / call$0.003
Success—
StatusUnknown
How this service works
Use this service before importing CSV to check data quality: missing cells, duplicate rows, required columns and declared string, integer, number, boolean or date types. Provide raw CSV with unique headers; comma, semicolon and tab delimiters supported. Max 1 MiB UTF-8, 10000 data rows and 100 columns. No URL fetching. Invalid business data returns valid=false.
Output
Returns a structured JSON object indicating whether the CSV is valid (valid: true/false), along with details of any detected issues: missing cells by row and column, duplicate row indices, columns that fail declared type checks, and any declared required columns that are absent.
Example use cases
Pre-import ETL data quality gate
“I have a CSV export from our CRM that I'm about to load into the warehouse — can you analyze it for any missing cells, duplicate rows, or type mismatches? The 'customer_id' column should be integers and 'signup_date' should be dates, and both plus 'email' are required. It's comma-delimited.”
User-uploaded file validation
“A user just uploaded a semicolon-delimited CSV to our platform — before we process it, check that the required columns 'product_sku', 'quantity', and 'price' are all present, 'quantity' is an integer, 'price' is a number, and there are no blank cells or duplicate rows.”
Automated nightly data feed check
“We get a nightly tab-delimited CSV feed from our supplier — can you run a data quality check on it to make sure there are no missing values, all rows are unique, and the 'unit_cost' column contains numbers while 'in_stock' is boolean?”
Technical details
Endpoint
POSThttps://api.coregroupimmo.com/v1/csv/analyze?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, structured pre-import quality check on a CSV file before loading it into a database, data warehouse, or application. It is ideal for ETL pipelines, user-uploaded file validation workflows, and automated data feed audits where catching missing values, type errors, duplicate rows, and missing required columns early prevents downstream failures. Prefer it over generic parsers when you need machine-readable per-row, per-column error reports.
Side effects
- CSV exceeds 1 MiB size limit — request rejected
- More than 10,000 data rows — request rejected
- More than 100 columns — request rejected
- Duplicate or empty column headers — request rejected
- Invalid UTF-8 encoding — parsing failure
- Delimiter mismatch causes all rows to appear as single-column — misleading results
- Business data logic issues (e.g. wrong types throughout) — returns valid=false with details
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Raw CSV, at most 1 MiB UTF-8, 10000 data rows, 100 columns; unique non-empty headers. |
| types | object | |
| delimiter | string | |
| requiredColumns | array |
Output
Schema · Response
{
"type": "json",
"example": {
"result": {
"valid": true,
"counts": {
"rows": 1,
"columns": 2,
"typeErrors": 0,
"missingCells": 0,
"duplicateRows": 0,
"missingRequiredColumns": 0
},
"errors": [],
"truncated": false
},
"service": "core-csv-analyzer"
}
}Tags
x402
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