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Delx Commerce CSV Schema Inferrer
x402
Delx Commerce CSV Schema Inferrer is a paid API for AI agents from commerce.delx.ai, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-01).
Infers column names and data types from a raw CSV text sample, returning a structured schema description.
Price / call$0.001
Success—
StatusUnknown
How this service works
Pay-per-result APIs for agents. No signup. Exact price. Verifiable delivery. USDC on Base + Solana via x402.
Output
A JSON object with a schema identifier and an array of column descriptors, each containing the column name and a dtype_hint (e.g. 'number', 'string', 'boolean', 'date') inferred from the CSV sample provided.
Example use cases
Auto-detect pipeline column types
“I've got a raw CSV export from our database and need to know what data types each column is before I load it into BigQuery — can you infer the schema from this sample: 'user_id,email,age,signup_date\n101,alice@example.com,29,2023-01-15'?”
Validate CSV before ETL import
“Before I run this ETL job, can you check what column types are in this CSV snippet so I can make sure nothing unexpected is coming through? Here's the sample: 'product_id,price,in_stock\n42,19.99,true\n43,5.00,false'”
Understand unknown CSV structure
“Someone sent me a CSV file and I have no idea what's in it — can you analyze this sample and tell me the column names and what kind of data each one holds? 'lat,lon,label\n40.71,-74.00,NYC\n34.05,-118.24,LA'”
Technical details
Endpoint
POSThttps://commerce.delx.ai/api/v1/x402/csv-schema-infer?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, cheap ($0.001 USDC), no-signup schema inference from raw CSV text — especially in agent pipelines where you need to programmatically understand tabular data structure before loading, validating, or transforming it. Prefer this over manual parsing or heavier data profiling tools when a lightweight dtype hint per column is sufficient.
Side effects
- Malformed or empty CSV input returns an error or empty columns array
- Ambiguous columns with mixed types may receive a generic dtype_hint like 'string'
- Very short samples (single row) may produce inaccurate dtype hints
- Non-UTF-8 encoded CSV text may cause parsing failures
- Missing column headers may result in auto-generated column names
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| csv | string | CSV text sample |
Output
Schema · Response
{
"type": "json",
"example": {
"schema": "delx/util-csv-schema-infer/v1",
"columns": [
{
"name": "a",
"dtype_hint": "number"
},
{
"name": "b",
"dtype_hint": "string"
}
]
}
}Tags
x402
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