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genpark-agentic-zero-knowledge-pii-token-vault-masker-skill

genpark-agentic-zero-knowledge-pii-token-vault-masker-skill

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genpark-agentic-zero-knowledge-pii-token-vault-masker-skill

Official Hub LinkedIn Zero External Dependencies MCP Compliant License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Enterprise Zero-Knowledge PII Tokenization Vault & Prompt Masker. Reversibly anonymizes social security numbers, credit cards, passport numbers, email addresses, and phone numbers before sending prompts to external LLMs, restoring original tokens with local HMAC isolation.

🌐 Official GenPark AI Ecosystem Links

Related MCP server: SecureMCP

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.

  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.

  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.

  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import ZeroKnowledgePIITokenVaultMasker

def main():
    masker = ZeroKnowledgePIITokenVaultMasker()
    prompt = "Process refund for card 4111-2222-3333-4444 and email receipt to alice@company.org."
    res = masker.mask_prompt_pii(prompt)
    print("--- Zero-Knowledge PII Masking ---")
    print(f"Masked Prompt: {res['masked_prompt']}")
    print(f"Detected PII Count: {res['detected_pii_count']}")
    
    mock_llm_response = f"Refund confirmed for {list(res['token_vault'].keys())[0]}."
    unmasked = masker.unmask_completion_pii(mock_llm_response, res['token_vault'])
    print(f"Restored Completion: {unmasked}")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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