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Mekler AI CSV Column Spec Validator
x402
Mekler AI CSV Column Spec Validator is a paid API for AI agents from api.mekler.ai, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-10).
Validates a JSON column specification (types, constraints, row bounds) before using it with the CSV data validation endpoint
Price / call$0.05
Success—
StatusUnknown
How this service works
x402 service diagnostics, annual SEC financial facts, CSV validation, citation-source comparison, web extraction and email domain checks for agents. Retrieve six annual US-GAAP USD fields by CIK with filing links, same-filing growth checks and optional observed revisions; check supplied CSV and citations; extract public page text, rendering JavaScript in a headless browser when needed; inspect email address syntax and mail-domain DNS. Pay per call with native USDC on Base or Solana via x402, without a separate API key. Wallet-first free trials cover CSV, column-spec, citation and static-page operations.
Output
Returns the validated column specification including the columns array with all field definitions, schema_version, a boundary statement clarifying typed-only constraints, and an unsupported list of any unrecognized fields that were rejected.
Example use cases
Pre-flight spec check for ETL pipeline
“I'm about to set up an ETL import and want to make sure my column spec is valid before wiring it up — columns are 'user_id' (string, required, unique), 'email' (string, required), and 'score' (integer, min 0, max 100, required). Can you validate that spec?”
Validating enum constraints on a status column
“Can you check if this column spec is acceptable: one column called 'status' of type string with allowed values 'active', 'inactive', 'pending', and it must be required. I want to make sure the enum list is valid before I assign it to my CSV extractor.”
Confirming row bounds and optional columns spec
“I want to validate a column spec that has a 'date' column of type date and a 'revenue' column of type decimal with min 0, and I want to allow up to 500 rows minimum and 2000 rows maximum with extra columns allowed. Please check if that spec is valid.”
Technical details
Endpoint
POSThttps://api.mekler.ai/v1/data/csv/validate-column-spec?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to verify a column specification before submitting it to the /v1/data/csv/validate-data endpoint, or any time you want to catch schema configuration errors early in an automated pipeline. Prefer this over directly submitting to the data validator when you want to confirm that column types, constraints, and row bounds are correctly structured without incurring the cost or complexity of running against real CSV data.
Side effects
- Unknown or unsupported column field names cause rejection with indication of which fields are invalid
- Unsupported column types (anything other than string, integer, decimal, boolean, date) are rejected
- min/max bounds specified on non-numeric columns (e.g. string or boolean) are rejected
- enum lists exceeding 100 items or with items over 1000 characters are rejected
- More than 50 columns in a single spec triggers rejection
- max_rows or min_rows outside 0-2000 range causes rejection
- Missing required 'columns' array or missing 'name'/'type' on individual columns causes rejection
- Natural language or prose-based rule descriptions are not supported and will be rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| requirements | object |
Output
Schema · Response
{
"type": "json",
"example": {
"spec": {
"columns": [
{
"name": "id",
"type": "string",
"unique": true,
"required": true
},
{
"max": "100",
"min": "0",
"name": "amount",
"type": "decimal",
"required": true
}
],
"min_rows": 1
},
"boundary": "Typed requirements only; no natural-language interpretation.",
"unsupported": [],
"schema_version": 1
}
}Tags
x402
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