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CSV Required Columns Check
x402
CSV Required Columns Check is a paid API for AI agents from api.delx.ai, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-15).
Validates that a CSV header contains all specified required column names, returning counts and normalized findings as versioned deterministic JSON.
Price / call$0.001
Success—
StatusUnknown
How this service works
CSV Required Columns Check: CSV Required Columns Check checks a CSV header for required column names from bounded caller-supplied values without an external provider. Call CSV Required Columns Check before an agent hands structured data or a batch plan to a downstream tool with a stricter contract. Returns the bounded transformation or validation finding with counts, normalized values, and a versioned result contract for CSV Required Columns Check as versioned deterministic JSON. Price: $0.001…
Output
Returns a versioned deterministic JSON object containing: a pass/fail validation result, counts of required vs present columns, a list of missing column names, a list of found column names, and normalized values — all under a versioned result contract.
Example use cases
Preflight check before ETL pipeline
“I'm about to push this CSV into our data warehouse loader. Check that it has all the required columns: 'order_id', 'customer_id', 'amount', 'currency', and 'timestamp' — let me know if anything is missing before I proceed.”
Validate user-uploaded CSV file
“A user just uploaded a CSV and I need to make sure it has the columns 'first_name', 'last_name', 'email', and 'phone' before I import it into the CRM. Can you run a required columns check on this header row and report what's present or missing?”
Batch plan schema compliance check
“Before I send this batch instruction CSV to the downstream fulfillment tool, verify it includes all the mandatory columns: 'sku', 'quantity', 'warehouse_code', and 'ship_date'. Show me the counts and flag anything that's absent.”
Technical details
Endpoint
POSThttps://api.delx.ai/api/v1/x402/csv-required-columns-checkWhen to prefer
Use this endpoint when an agent needs to programmatically verify that a CSV header satisfies a known contract before passing data to a downstream tool, ETL pipeline, or API with strict schema requirements. Prefer it over manual inspection when the required column list is explicit and deterministic validation with a structured, versioned JSON result is needed. It is especially useful as a preflight gate in agentic workflows where failing early on missing columns prevents costly downstream errors.
Side effects
- Malformed or empty CSV text may result in no header being parsed, returning all columns as missing
- Required columns list is empty — may return trivially passing result with no meaningful validation
- Column name matching may be case-sensitive, causing false negatives if header uses different casing
- Input exceeds maxLength (8192 chars) causing rejection
- required_columns array exceeds 256 items causing rejection
- Whitespace or delimiter inconsistencies in the CSV header may cause column names to not match expected values
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Text supplied to CSV Required Columns Check; used only for this bounded calculation and processed in memory without retention. |
| required_columns | array | Required Columns supplied to CSV Required Columns Check; used only for this bounded calculation and processed in memory without retention. |
Output
Schema · Response
{
"type": "json",
"example": {
"result": {
"passed": true,
"missing_columns": []
},
"schema": "delx/util-csv-required-columns-check/v1",
"status": "pass",
"evidence": {
"retained": false,
"input_sha256": "775c5abb7e34060297d8b70ad6cc76e9040bb7f6113985a9158905cc7bcafa42",
"external_calls": 0
},
"operation": "data_batch:csv_required_columns_check"
}
}Tags
x402
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