PII Anonymizer — MCP Server

An MCP server that lets AI assistants (Claude, ChatGPT, Cursor, etc.) automatically detect and redact PII before processing sensitive text.

All processing is local. Zero network calls. No data leaves the machine.

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Quick Start (2 minutes)

# 1. Clone and install
git clone https://github.com/kofi-sketch/pii-anonymizer-mcp.git
cd pii-anonymizer-mcp
npm install

# 2. Verify it works
npm test

You should see:

✓ Detected 6 PII items
✓ anonymize_text works
✓ deanonymize_text works
✓ All tests passed

That's it. The server is ready.

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Connect to Your AI Client

The server runs over stdio (standard MCP transport). Add it to whichever client you use:

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "pii-anonymizer": {
      "command": "node",
      "args": ["/absolute/path/to/pii-anonymizer-mcp/server.js"]
    }
  }
}

Cursor

Edit .cursor/mcp.json in your project root (or global settings):

{
  "mcpServers": {
    "pii-anonymizer": {
      "command": "node",
      "args": ["/absolute/path/to/pii-anonymizer-mcp/server.js"]
    }
  }
}

VS Code (GitHub Copilot)

Add to your VS Code settings.json:

{
  "mcp": {
    "servers": {
      "pii-anonymizer": {
        "command": "node",
        "args": ["/absolute/path/to/pii-anonymizer-mcp/server.js"]
      }
    }
  }
}

Any Other MCP Client

stdio transport — pipe JSON-RPC 2.0 over stdin/stdout:

node server.js

Note: Replace /absolute/path/to/ with the actual path where you cloned the repo. Run pwd in the repo directory to get it.

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What You Get: 6 Tools

anonymize_text

Pass any text. Get back sanitized text + an entity map.

Input:  "Hi, I'm John Smith. SSN: 123-45-6789. Email: john@example.com"
Output: "Hi, I'm [PERSON_NAME_1] [PERSON_NAME_2]. SSN: [SSN]. Email: [EMAIL]"

The entity map lets you reverse it later:

{
  "[PERSON_NAME_1]": "John",
  "[PERSON_NAME_2]": "Smith",
  "[SSN]": "123-45-6789",
  "[EMAIL]": "john@example.com"
}

Parameters:

• text (required) — the text to anonymize
• detectors (optional) — array of detector IDs to use (default: all). Get IDs from list_detectors.

deanonymize_text

After the AI processes the sanitized text, restore the originals:

Input:  "[PERSON_NAME_1]'s account has been updated" + entityMap
Output: "John's account has been updated"

Parameters:

• anonymizedText (required) — text with placeholders
• entityMap (required) — the map from anonymize_text

list_detectors

Returns all 30+ detector patterns with IDs, categories, and descriptions. Use this to selectively enable/disable detectors.

add_custom_names

Add your own names, usernames, codenames, or any terms that should be flagged as PII:

{ "names": ["Satoshi", "Nakamoto", "kraken_admin_42", "ProjectPhoenix"] }

These persist for the session and get detected as [PERSON_NAME].

add_custom_patterns

Add custom regex patterns for organization-specific PII — employee IDs, internal codes, ticket numbers:

{
  "patterns": [
    { "regex": "EMP-\\d{6}", "label": "Employee ID", "placeholder": "EMPLOYEE_ID" },
    { "regex": "PROJ-[A-Z]{3}-\\d{4}", "label": "Project Code", "placeholder": "PROJECT_ID" },
    { "regex": "TICKET-\\d+", "label": "Support Ticket", "placeholder": "TICKET_ID" }
  ]
}

clear_custom_dictionaries

Reset all custom names and patterns. Built-in detectors are unaffected.

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What It Detects

Category	Examples
Names	6,000+ first/last names across 40+ cultures. Context-aware — won't flag "Will" in "will do" but catches "Dear Will,"
Financial	Credit cards (Luhn-validated), IBANs, routing numbers, account numbers, UK sort codes
Identity	US SSNs, UK NI numbers, passport numbers, driver's licenses, dates of birth
Contact	Email addresses, international phone numbers, street addresses, UK postcodes, US ZIP codes
Crypto/Keys	Ethereum/Bitcoin addresses, BIP-39 seed phrases, JWT tokens, API keys (Stripe, GitHub, GitLab, Slack)
System	UUIDs, session tokens, device IDs, user IDs

Plus context-aware classification — catches standalone numbers near keywords like "account", "routing", "SSN" even without a fixed format.

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Typical Workflow

1. User pastes sensitive text into AI chat
2. AI calls anonymize_text → gets clean text + entity map
3. AI processes the sanitized text (summarize, classify, extract, translate, etc.)
4. AI calls deanonymize_text → restores originals in the output
5. User gets the result with real data intact

The user never has to manually scrub anything. The AI handles it automatically.

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CLI Tools

Two standalone command-line tools included — no MCP client needed.

pii-anonymize — Scrub & Restore

# Pipe text
echo "John Smith, SSN 123-45-6789" | node cli.js
# → [PERSON_NAME_1] [PERSON_NAME_2], SSN [SSN]

# Scrub a file, save entity map for later
node cli.js --file ticket.txt -o clean.txt --map map.json

# Restore originals after AI processing
node cli.js --restore --file ai-response.txt --map map.json

# Full JSON output (anonymized text + entity map + stats)
node cli.js --file data.txt --json

# Use custom org dictionary
node cli.js --config kraken-pii.json --file logs.txt

# Only run specific detectors
node cli.js --detectors email,credit_card,ssn --file data.csv

# Stats only
node cli.js --stats --file data.txt

pii-scan — Read-Only Scanner

Reports PII findings without modifying anything. Useful for audits and CI/CD gates.

# Scan a file
node scan.js --file ticket.txt

# ⚠ ticket.txt: 6 PII items found
#   PERSON_NAME (2)
#     → J***
#     → S****
#   SSN (1)
#     → ***6789
#   CREDIT_CARD (1)
#     → ***0366
#   Severity: HIGH

# Show context around each finding
node scan.js --file ticket.txt --verbose

# JSON report
node scan.js --file data.csv --json

# CI/CD gate — exit code 0=clean, 1=PII found
node scan.js --file output.txt -q || echo "BLOCKED: PII detected"

# Batch scan a directory
find ./logs -name "*.txt" -exec node scan.js --file {} -q \;

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Custom Dictionaries

Add your organization's own names and patterns via a config file.

Create pii-config.json next to server.js (auto-loaded on startup):

{
  "names": ["Jesse Powell", "internal_admin_42"],
  "nameFiles": ["employees.txt", "contractors.csv"],
  "patterns": [
    { "regex": "EMP-\\d{6}", "label": "Employee ID", "placeholder": "EMPLOYEE_ID" },
    { "regex": "TICKET-\\d+", "label": "Support Ticket", "placeholder": "TICKET_ID" }
  ]
}

• names — inline list of names/terms to flag
• nameFiles — paths to text files (one name per line) or CSVs
• patterns — custom regex with label and placeholder type

For the CLI, pass --config /path/to/config.json. For the MCP server, pass --config= as an arg or drop pii-config.json next to server.js.

See pii-config.example.json for a full example.

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Works With Everything

This tool uses MCP (Model Context Protocol) — an open standard. It works alongside any other MCP server or data tool your team already uses.

┌─────────────────────────────────────────────────────┐
│                   AI Client (Claude, Cursor, etc.)   │
│                                                      │
│  "Get support tickets from Superset,                 │
│   summarize them, but scrub PII first"               │
└──────┬──────────────────────────┬────────────────────┘
       │                          │
       ▼                          ▼
┌──────────────┐          ┌──────────────────┐
│  Superset    │          │  PII Anonymizer  │
│  MCP Server  │          │  MCP Server      │
│  (your data) │          │  (scrubs PII)    │
└──────────────┘          └──────────────────┘

Example flow:

1. AI pulls query results from Superset (or DataHub, Postgres, Slack, any MCP server)
2. AI passes the results through anonymize_text → PII gets replaced
3. AI processes the clean data (summarize, classify, extract)
4. AI calls deanonymize_text → originals restored in final output

The CLI tools work the same way with any pipeline:

# Superset export → scrub → feed to AI
superset export --query 42 | pii-anonymize -o clean.csv --map map.json

# Scan any data source for PII without changing it
cat database_dump.csv | pii-scan

# CI/CD: block deploys that leak PII
pii-scan --file api-response.json -q || exit 1

It doesn't need to know what your other tools are. It just processes whatever text passes through it — queries, logs, tickets, chat transcripts, API responses, database exports. If it's text, it can be scrubbed.

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Architecture

┌─────────────┐     stdio (JSON-RPC)     ┌──────────────────┐
│  MCP Client  │ ◄──────────────────────► │  pii-anonymizer  │
│  (Claude,    │                          │  server.js       │
│   Cursor,    │    anonymize_text()      │  engine.js       │
│   VS Code)   │    deanonymize_text()    │  (all local)     │
└─────────────┘    list_detectors()       └──────────────────┘
                                                   │
                                            No network calls
                                            No external APIs
                                            No data storage

• 2 files that matter: server.js (MCP wrapper) and engine.js (detection logic)
• 1 dependency: @modelcontextprotocol/sdk (the MCP protocol library)
• Runs in-process — no Docker, no cloud, no accounts

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Docker

Run containerised — prints to STDOUT, no network required.

# Build
docker build -t pii-anonymizer .

# Pipe text through the container
echo "John Smith, SSN 123-45-6789" | docker run -i pii-anonymizer

# Scrub a file (mount it in)
docker run -i pii-anonymizer < ticket.txt > clean.txt

# JSON output
echo "John Smith, SSN 123-45-6789" | docker run -i pii-anonymizer cli.js --json

# Read-only scan
echo "John Smith, SSN 123-45-6789" | docker run -i pii-anonymizer scan.js

# MCP server mode (for AI client integration)
docker run -i pii-anonymizer server.js

# With custom config (mount your config file)
docker run -i -v /path/to/pii-config.json:/app/pii-config.json pii-anonymizer

# CI/CD gate — exit code 1 if PII found
echo "some text" | docker run -i pii-anonymizer scan.js -q || echo "BLOCKED"

Image: node:18-alpine (~50MB). Zero network calls at runtime. No volumes needed unless using custom dictionaries.

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Requirements

• Node.js 18+ (or Docker)
• That's it. No API keys, no accounts, no network.

License

MIT

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Built by @kofi.owusu on Slack