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

[![Official Hub](https://img.shields.io/badge/GenPark-Official%20Hub-6366f1?style=for-the-badge&logo=compass&logoColor=white)](https://www.genpark.ai)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow?style=for-the-badge)](LICENSE)

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

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## 🌐 Official GenPark AI Ecosystem Links

- **GenPark Official Website**: [https://www.genpark.ai](https://www.genpark.ai)
- **LinkedIn Official Page**: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) ([https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550))

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## 🌟 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

```python
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`:

```bash
python mcp_server.py
```

Execute embedded test harness:

```bash
python mcp_server.py --test
```

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## 🛠️ MCP Tool Specification

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

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## 📜 License & Community

Licensed under the [MIT License](LICENSE). Copyright © 2026 [GenPark AI](https://www.genpark.ai).
Follow us on LinkedIn: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) | [https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550)