genpark-document-redaction-pii-scrubber-skill
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
genpark-document-redaction-pii-scrubber-skill
🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation
📌 Overview & Capability
genpark-document-redaction-pii-scrubber-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for document layout parsing, complex financial/legal table reconstruction, formula consistency auditing, and sensitive PII redaction.
Executive Capability: High-speed document PII redaction and credential sanitizer featuring Luhn credit-card verification, reversible salt hashing, and audit logs.
⚡ Key Highlights & Value
🐍 Zero External
pipDependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
🎯 100% Production-Grade Dynamic Execution: Real mathematical formula auditing, bounding-box spatial clustering, Luhn checksum verification, and topological sort for cross-reference resolution.
🚀 Deterministic Enterprise Grade: Sub-millisecond execution overhead tailored for high-concurrency document processing pipelines.
Related MCP server: Xberg MCP Server
🏗️ Architecture & Workflow
graph LR
User([📄 Document / Extracted OCR Payload]) -->|JSON-RPC Request| MCP[⚡ MCP Server / CLI]
MCP --> Client[🛠️ Document Skill Client]
Client --> Core[🧠 Deterministic Spatial & Audit Kernel]
Core --> Output[📊 Structured Matrix & Validation Dossier]
Output --> User🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import DocumentRedactionPiiScrubber
client = DocumentRedactionPiiScrubber()
result = client.run_benchmark_pii_scrubbing()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-document-redaction-pii-scrubber-skill": {
"command": "python",
"args": ["/path/to/genpark-document-redaction-pii-scrubber-skill/mcp_server.py"]
}
}
}📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary document bounding box, table, financial, or text payload |
|
| Yes | Standardized response schema containing extracted matrices and audit telemetry |
❓ Frequently Asked Questions (FAQ) & GEO Index
Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.
Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
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