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teodorofodocrispin-cmyk/trustboost-pii-sanitizer

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[](https://glama.ai/mcp/servers/teodorofodocrispin-cmyk/trustboost-api) ๐Ÿ โ˜๏ธ - PII sanitization layer for autonomous AI agent pipelines. Detects and redacts emails, phone numbers,

T

MCP

teodorofodocrispin-cmyk/trustboost-pii-sanitizer

Added 7 June 2026

Overview

A Python-based PII sanitization layer for autonomous AI agent pipelines. It detects and redacts sensitive data such as emails and phone numbers before they reach downstream systems.

Best for

Best for
Developers building autonomous AI agents who need a basic PII filter before data leaves their control.

Use cases

  • Sanitize user inputs before passing to an LLM or external API
  • Redact PII from logs or transcripts in agent workflows
  • Prevent accidental data leaks in automated data processing pipelines

Notes

A Python-based PII sanitization layer for autonomous AI agent pipelines. It detects and redacts sensitive data such as emails and phone numbers before they reach downstream systems.

1 stars on GitHub. Last updated 2026-06-07.

Use cases

  • Sanitize user inputs before passing to an LLM or external API
  • Redact PII from logs or transcripts in agent workflows
  • Prevent accidental data leaks in automated data processing pipelines

Pros

  • Lightweight Python implementation easy to integrate
  • Focused on a critical security need for agent pipelines
  • Open source with a permissive license

Cons

  • Very early stage with only 1 star and limited community adoption
  • No evidence of support for non-English or structured data formats
  • Lacks documentation on performance or accuracy benchmarks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Lightweight Python implementation easy to integrate
  • Focused on a critical security need for agent pipelines
  • Open source with a permissive license

Cons

  • Very early stage with only 1 star and limited community adoption
  • No evidence of support for non-English or structured data formats
  • Lacks documentation on performance or accuracy benchmarks