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CSV Unpivot
x402
CSV Unpivot is a paid API for AI agents from api.delx.ai, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Converts wide-format CSV columns into long-format name/value row pairs, returning normalized row counts and a versioned deterministic result.
Price / call$0.001
Success—
StatusUnknown
How this service works
CSV Unpivot: CSV Unpivot converts selected wide CSV columns into name and value rows from bounded caller-supplied values without an external provider. Call CSV Unpivot 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 Unpivot as versioned deterministic JSON. Price: $0.001 USDC via x402 on Base. First-party, stateless,…
Output
A versioned deterministic JSON object containing the unpivoted rows as name-value pairs, counts of input and output rows, normalized values, and a result contract version identifier. No data is retained after processing.
Example use cases
Analytics pipeline data reshaping
“I have a wide CSV with columns 'user_id' and 'date' as identifiers, and then 'clicks', 'impressions', 'conversions' as metrics — can you unpivot it so each metric becomes its own row with a name and value column?”
Database import normalization
“Before I load this survey CSV into our database, I need to reshape it — 'respondent_id' and 'age_group' should stay as id columns, and all the question columns like 'q1', 'q2', 'q3' should be unpivoted into name-value rows.”
Downstream tool contract preparation
“I need to send this sales CSV to a downstream tool that only accepts long-format data — keep 'store_id' and 'month' as id columns and melt 'north_sales', 'south_sales', 'east_sales', 'west_sales' into rows.”
Technical details
Endpoint
POSThttps://api.delx.ai/api/v1/x402/csv-unpivotWhen to prefer
Choose this endpoint when you have wide-format CSV data that needs to be reshaped into long/tall format before passing to a downstream tool with a strict row-level contract. It is stateless and deterministic, making it safe for idempotent pipelines. Prefer it over general-purpose code execution when you need a cheap, fast, versioned reshaping step without spinning up a compute environment.
Side effects
- Column names in id_columns or value_columns not found in the CSV header — returns validation error with unmatched column names
- Malformed or unparseable CSV text — returns parsing error
- Input exceeds 8192-character text limit — returns size limit error
- Empty value_columns array — returns validation finding with zero unpivoted rows
- Overlapping id_columns and value_columns — returns conflict error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Text supplied to CSV Unpivot; used only for this bounded calculation and processed in memory without retention. |
| id_columns | array | Id Columns supplied to CSV Unpivot; used only for this bounded calculation and processed in memory without retention. |
| value_columns | array | Value Columns supplied to CSV Unpivot; used only for this bounded calculation and processed in memory without retention. |
Output
Schema · Response
{
"type": "json",
"example": {
"result": {
"csv": "id,variable,value\n1,q1,10\n1,q2,20",
"row_count": 2
},
"schema": "delx/util-csv-unpivot/v1",
"status": "pass",
"evidence": {
"retained": false,
"input_sha256": "1664e3ca93eaf50bde3e9b8dfa73ca0237be72fb4f663837d3df1087aa684d02",
"external_calls": 0
},
"operation": "data_batch:csv_unpivot"
}
}Tags
x402
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