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Jarvis X402 CSV Data Cleaner
x402
Jarvis X402 CSV Data Cleaner is a paid API for AI agents from api.jarvis-x402.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Cleans and normalizes raw CSV data in-memory: standardizes column names, infers types, removes duplicates, and fills or flags empty values, returning JSON.
Price / call$0.02
Success—
StatusUnknown
How this service works
Limpieza y normalizacion de datos CSV: nombres de columnas, tipos, duplicados y vacios. Los datos no se almacenan, solo se procesan en memoria.
Output
Returns a JSON object containing the cleaned and normalized data from the input CSV, with standardized column names (e.g. lowercased, snake_cased), inferred data types per column, duplicate rows removed, and empty/null values flagged or handled. The output schema matches the `to: json` format.
Example use cases
CRM export cleanup before import
“I just exported a CSV from our old CRM and it's full of duplicate contacts, weird column casing, and blank phone numbers. Clean it up and give me back structured JSON so I can import it into the new system. Here's the raw CSV: 'First Name,LAST NAME,Phone,Email\nJohn,Doe,,john@acme.com\nJohn,Doe,,john@acme.com\nJane,Smith,555-1234,jane@acme.com'”
Survey results normalization
“I collected survey responses in a CSV and the column headers are inconsistent — some are camelCase, some have spaces, and there are a bunch of empty answers. Can you normalize the column names, drop duplicates, and convert it to clean JSON? CSV content: 'respondentId,Full Name,age ,q1Answer,Q2_answer\n1,Alice,28,Yes,\n2,Bob,,No,Maybe\n1,Alice,28,Yes,'”
Data pipeline preprocessing step
“Before I load this product inventory CSV into our analytics pipeline, I need it cleaned — standardize the column names, fix any type inconsistencies, remove duplicate SKUs, and fill or flag any empty price fields. Here's the raw CSV: 'SKU,Product Name,Price,Stock,category\nABC1,Widget A,9.99,100,Electronics\nABC1,Widget A,,100,Electronics\nDEF2,Gadget B,24.99,,gadgets'”
Technical details
Endpoint
POSThttps://api.jarvis-x402.com/limpiar-datos-pagoWhen to prefer
Choose this endpoint when you need lightweight, in-memory CSV cleaning and normalization without storing data — ideal for ETL preprocessing, quick data sanitization before database ingestion, or converting messy CSV exports to structured JSON. Prefer it over heavier data pipeline tools when privacy matters (no data retention) and the task is per-file batch cleaning rather than streaming or database-level operations.
Side effects
- Malformed or non-parseable CSV input returns an error
- Missing required fields (input, from, to) causes a 400-level error
- Very large CSV payloads may time out or be rejected
- Non-CSV input passed as raw string may produce unexpected normalization results
- Payment failure (x402 protocol) prevents processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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