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Netzhandwerker Data Cleaner
x402
Netzhandwerker Data Cleaner is a paid API for AI agents from tools.netzhandwerker.de, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).
Cleans, deduplicates, and normalizes CSV or row data by trimming whitespace, removing empty rows/columns, and inferring data types
Price / call$0.002
Success—
StatusUnknown
How this service works
x402-Werkzeuge für autonome Agenten.
Output
Returns a JSON object containing: cleaned rows array, a report with counts of rows/columns in and out, duplicates removed, values trimmed, and empty rows/columns dropped, plus a schema object with inferred types and null counts for each column, and the source format detected.
Example use cases
Pre-import CRM data cleanup
“I have a raw CSV export from our old CRM with lots of blank rows, duplicate entries, and extra whitespace — clean it up, dedupe it, and tell me what columns were dropped before I import it into the new system.”
Sales report normalization
“Here's a sales data CSV with mixed formatting and some empty columns like 'notiz' — trim all the values, remove duplicates, infer the column types, and give me back the cleaned rows along with a summary of what changed.”
JSON row list deduplication
“I have a list of up to 5000 JSON objects from our data pipeline that may have duplicate entries and empty fields — can you clean them up, drop empty columns, and return the deduped result with a report on how many rows were removed?”
Technical details
Endpoint
POSThttps://tools.netzhandwerker.de/v1/data/cleanWhen to prefer
Choose this endpoint when you need lightweight, automated CSV or tabular data cleaning with type inference and a structured cleaning report — especially useful in ETL pipelines, before database imports, or when preparing datasets for analysis. It handles both raw CSV text and pre-parsed JSON row arrays, making it versatile for agent workflows that receive data in either format.
Side effects
- Invalid or unparseable CSV text returns an error
- Exceeding 20,000 rows limit may cause rejection
- Ambiguous delimiter not auto-detected if malformed CSV
- Empty input with no csv or rows field returns error
- Conflicting csv and rows fields may cause unexpected behavior
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| csv | string | CSV-Text, Trennzeichen wird erkannt |
| rows | array | Alternativ: Liste von Objekten |
| trim | boolean | |
| dedupe | boolean | |
| delimiter | string | Trennzeichen erzwingen |
| drop_empty_rows | boolean | |
| drop_empty_columns | boolean |
Output
Schema · Response
{
"type": "json",
"example": {
"rows": [
{
"datum": "2026-03-01",
"firma": "Bau GmbH",
"umsatz_2025": 1234.5
},
{
"datum": "2026-04-15",
"firma": "Holz AG",
"umsatz_2025": 980
}
],
"report": {
"rows_in": 3,
"rows_out": 2,
"columns_in": 4,
"columns_out": 3,
"type_changes": {},
"values_trimmed": 0,
"duplicates_removed": 1,
"empty_rows_removed": 0,
"empty_columns_removed": [
"notiz"
]
},
"schema": {
"datum": {
"type": "date",
"mixed": false,
"null_count": 0
},
"firma": {
"type": "string",
"mixed": false,
"null_count": 0
},
"umsatz_2025": {
"type": "number",
"mixed": false,
"null_count": 0
}
},
"source": "csv",
"columns": [
"firma",
"umsatz_2025",
"datum"
]
}
}Tags
x402
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