Netzhandwerker PII Redaction

x402

Netzhandwerker PII Redaction is a paid API for AI agents from tools.netzhandwerker.de, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).

Detects and redacts personally identifiable information (PII) such as emails, IBANs, and phone numbers from text using masking, labeling, or hashing modes.

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

x402-Werkzeuge für autonome Agenten.

Output

Returns a JSON object containing the redacted version of the input text, a list of findings with character positions, line/column numbers, preview snippets, and category labels, plus a by_category count summary and the redaction mode used. Optionally includes the original found values if reveal=true.

Example use cases

GDPR-safe customer message logging

“Before we store this support ticket in our database, redact all PII from it using label mode so we replace each item with a category placeholder like [EMAIL] or [PHONE]: 'Contact John at john.doe@company.com or call +1-800-555-0199.'”

Financial document pseudonymization

“I need to hash all IBANs in this payment instruction text so they're replaced with stable identifiers but not visible in plaintext — only check the 'iban' category: 'Transfer to DE89 3704 0044 0532 0130 00 and CC DE75 5121 0800 1245 1261 99.'”

AI training data anonymization

“Strip personal info from this user-generated text before we use it for model training — use mask mode and also return the raw found values so I can audit what was detected: 'My name is Maria Schmidt, reach me at maria@schmidt.de or IBAN AT61 1904 3002 3457 3201.'”

Technical details

Endpoint

POSThttps://tools.netzhandwerker.de/v1/pii/redact

When to prefer

Choose this endpoint when you need fast, per-call PII redaction on arbitrary text strings with flexible output modes (mask, label, hash) and category filtering. It is well-suited for preprocessing user input, sanitizing logs, or anonymizing text before storage or AI processing. Prefer it over full NLP-based PII pipelines when deterministic pattern matching for structured PII types (emails, IBANs, phone numbers) is sufficient and low latency matters.

Side effects

Empty or missing text field returns an error or empty findings
Unknown category strings in the categories array may be silently ignored or cause a 400 error
Very long texts may time out or hit payload limits
Pattern-based detection may produce false positives for email-like or IBAN-like strings
False negatives are possible since detection is regex/pattern-based, not NLP name recognition
Invalid mode enum value returns a 400 validation error

Input

Schema · Request body

Field	Type	Description
mode	string	mask ersetzt teilweise, label setzt Platzhalter, hash setzt eine stabile Kennung
text	string	Zu pruefender Text
reveal	boolean	Fundwerte im Klartext zurueckgeben
categories	array	Nur diese Kategorien pruefen

Output

Schema · Response

{
  "type": "json",
  "example": {
    "mode": "mask",
    "note": "Mustererkennung, kein Namensverzeichnis.",
    "findings": [
      {
        "end": 37,
        "line": 1,
        "start": 13,
        "column": 14,
        "preview": "a**********@example.com",
        "category": "email"
      },
      {
        "end": 73,
        "line": 1,
        "start": 51,
        "column": 52,
        "preview": "******************3000",
        "category": "iban"
      }
    ],
    "by_category": {
      "iban": 1,
      "email": 1,
      "phone": 1
    },
    "redacted_text": "Bitte an a**********@example.com senden, IBAN ******************3000, Tel ********567.",
    "findings_count": 3
  }
}

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How this service works

x402-Werkzeuge für autonome Agenten.

Output

Example use cases

GDPR-safe customer message logging

Financial document pseudonymization

AI training data anonymization

Technical details

Endpoint

POSThttps://tools.netzhandwerker.de/v1/pii/redact

When to prefer

Side effects

Empty or missing text field returns an error or empty findings
Unknown category strings in the categories array may be silently ignored or cause a 400 error
Very long texts may time out or hit payload limits
Pattern-based detection may produce false positives for email-like or IBAN-like strings
False negatives are possible since detection is regex/pattern-based, not NLP name recognition
Invalid mode enum value returns a 400 validation error

Input

Schema · Request body

Field	Type	Description
mode	string	mask ersetzt teilweise, label setzt Platzhalter, hash setzt eine stabile Kennung
text	string	Zu pruefender Text
reveal	boolean	Fundwerte im Klartext zurueckgeben
categories	array	Nur diese Kategorien pruefen

Output

Schema · Response

{
  "type": "json",
  "example": {
    "mode": "mask",
    "note": "Mustererkennung, kein Namensverzeichnis.",
    "findings": [
      {
        "end": 37,
        "line": 1,
        "start": 13,
        "column": 14,
        "preview": "a**********@example.com",
        "category": "email"
      },
      {
        "end": 73,
        "line": 1,
        "start": 51,
        "column": 52,
        "preview": "******************3000",
        "category": "iban"
      }
    ],
    "by_category": {
      "iban": 1,
      "email": 1,
      "phone": 1
    },
    "redacted_text": "Bitte an a**********@example.com senden, IBAN ******************3000, Tel ********567.",
    "findings_count": 3
  }
}