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scrub-mcp PII Redaction API
x402
scrub-mcp PII Redaction API is a paid API for AI agents from scrub-mcp.malgorath.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-13).
Removes personally identifiable information (PII) from text using context-aware redaction across medical, legal, financial, and criminal domains
Price / call$0.01
Success—
StatusUnknown
How this service works
Paid x402 MCP server fronting the stateless PII scrub-api. Zero storage.
Output
Returns a JSON object with 'scrubbed_text' containing the original text with PII replaced by bracketed category labels (e.g. [NAME], [SSN], [DATE]), and a 'removed' map showing how many instances of each PII category were redacted.
Example use cases
HIPAA-safe medical record anonymization
“Please redact all PII from this patient summary before we share it with the research team — strip names, dates, SSNs, and phone numbers using the medical domain setting: 'Mary Johnson, DOB 01/15/1965, SSN 987-65-4321, was admitted on February 10th and discharged on February 14th. Contact: 555-234-5678.'”
Financial document PII scrub before audit
“I need to anonymize this loan application excerpt for our external auditors — remove names, account numbers, SSNs, and addresses using the financial domain: 'Applicant Robert Nguyen, SSN 456-78-9012, account #00123456, residing at 42 Maple Street, Springfield, applied on June 5th 2024.'”
Legal filing redaction for public record
“Strip all personal identifiers from this legal document snippet before we post it publicly — remove names, phone numbers, and addresses in legal domain context: 'Defendant Sarah O'Brien, residing at 19 Elm Avenue, called the plaintiff at 555-111-2222 on December 3rd 2023.'”
Technical details
Endpoint
POSThttps://scrub-mcp.malgorath.com/scrub?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need stateless, zero-storage PII redaction with domain-aware context (medical, legal, financial, criminal) and want per-category counts of what was removed. Ideal for HIPAA/GDPR compliance pipelines, anonymizing documents before sharing or storing, and preparing sanitized training data. Prefer it over generic regex-based scrubbers when context-sensitive redaction is needed, and over heavyweight on-premise solutions when serverless, pay-per-call pricing ($0.01/call) fits your usage pattern.
Side effects
- Missing required 'text' field returns a 400-level error
- Invalid 'strip' enum values cause schema validation failure
- Temperature out of range (0–2) causes rejection
- Empty string input may return empty scrubbed_text with zero removals
- Very long texts may exceed model context limits causing truncation or error
- Payment failure or insufficient USDC balance returns HTTP 402
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Text to scrub. Required. |
| strip | array | PII categories to remove. Defaults to all categories. |
| domain | string | Subject area for context-aware redaction. |
| temperature | number | Model temperature. |
Output
Schema · Response
{
"type": "json",
"example": {
"removed": {
"ssn": 1,
"dates": 2,
"names": 1
},
"scrubbed_text": "Patient [NAME], DOB [DATE], SSN [SSN] visited on [DATE]."
}
}Tags
x402
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