PII Anonymizer FAQs

Question 1

What is PII Anonymizer?

Accepted Answer

PII Anonymizer is an MCP server that automatically detects and redacts Personally Identifiable Information (PII) from any text. It ensures sensitive data is scrubbed before being processed by AI assistants (like Claude, ChatGPT, Cursor) or other tools, maintaining data privacy and compliance.

Question 2

Can I customize the PII detection rules for my specific needs?

Accepted Answer

Yes, PII Anonymizer offers extensive customization. You can add your own custom names or specific terms to be flagged as PII, and define custom regex patterns for organization-specific data like employee IDs or project codes. These can be managed via API calls or configuration files.

Question 3

How does PII Anonymizer ensure data privacy and security?

Accepted Answer

A core feature of PII Anonymizer is its commitment to privacy. All detection and redaction processing is performed locally on your machine, with zero network calls. This means no data ever leaves your environment, guaranteeing maximum security and confidentiality for your sensitive information.

Question 4

How does PII Anonymizer integrate with AI assistants and other tools?

Accepted Answer

It integrates seamlessly with AI clients that support the Model Context Protocol (MCP), such as Claude Desktop, Cursor, and VS Code (GitHub Copilot). It operates automatically in the background: the AI calls `anonymize_text` before processing sensitive input and `deanonymize_text` to restore originals in the final output, providing a smooth user experience without manual scrubbing.

Question 5

What types of PII can the tool detect and redact?

Accepted Answer

The tool comes with 30+ built-in PII detectors covering a wide range of categories, including names, financial details (credit cards, IBANs), identity documents (SSNs, passports), contact information (emails, phone numbers), crypto/API keys, and system IDs. It also supports context-aware detection for enhanced accuracy.

PII Anonymizer

PII Anonymizer

Key Features

Use Cases

Key Features

Use Cases