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CORE CSV Analyzer
x402
CORE CSV Analyzer is a paid API for AI agents from 92-222-181-125.sslip.io, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-17).
Validates raw CSV data for quality issues including missing cells, duplicate rows, required columns, and type conformance 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 result indicating whether the CSV is valid (valid: true/false), along with details of any issues found: missing cells by row/column, duplicate row indices, columns failing declared type checks, and any missing required columns.
Example use cases
Pre-import data quality gate
“I have a CSV of customer records I'm about to load into our CRM. Can you validate it first — check for missing cells, duplicates, and make sure 'customer_id' (integer), 'email' (string), and 'created_at' (date) columns are all there and properly typed? It's comma-delimited.”
ETL pipeline type conformance check
“Before the ETL job runs, validate this tab-delimited CSV export — I need 'amount' to be a number, 'is_active' to be a boolean, and 'transaction_date' to be a date. Flag any rows that break those types or have missing values.”
Spreadsheet duplicate and blank cell audit
“Can you analyze this semicolon-delimited CSV for me? I want to know if there are any duplicate rows or empty cells, and whether the required columns 'product_sku', 'price', and 'in_stock' are all present. Price should be a number and in_stock should be boolean.”
Technical details
Endpoint
POSThttps://92-222-181-125.sslip.io/v1/csv/analyze?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate a CSV file's structure and data quality before importing into a database, ETL pipeline, or any system that requires clean, well-typed tabular data. It is purpose-built for pre-import checks including missing values, duplicates, required columns, and column type conformance — covering string, integer, number, boolean, and date types. Prefer it over generic JSON or schema validators when your data is in CSV format. It is not suitable for fetching CSVs from URLs or handling files larger than 1 MiB.
Side effects
- CSV exceeds 1 MiB — request rejected with size error
- More than 10,000 data rows — truncation or rejection
- More than 100 columns — validation refused
- Duplicate or empty column headers — invalid input error
- Unsupported delimiter not in {comma, semicolon, tab} — schema validation error
- Malformed UTF-8 encoding — parse failure
- Required column declared but absent — returns valid=false with missing column detail
- Type mismatch for declared columns — returns valid=false with type error 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 |
Tags
x402
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