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 MCP Hub • 📦 GenPark Official • 📖 Documentation
genpark-personal-vault-zero-knowledge-sanitizer-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server engineered for next-generation personal agents, multi-agent frameworks, and autonomous developer workflows.
Executive Capability: Local-first zero-knowledge personal vault encryptor and PII sanitizer masking credentials, credit cards, and addresses before cloud LLM dispatch with client-side rehydration.
🐍 Zero External pip Dependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.
🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
Related MCP server: genpark-enterprise-compliance-pii-redaction-sentinel-skill
graph LR
Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-personal-vault-zero-knowledge-sanitizer-skill Server]
Server --> Core[🧠 Deterministic Processing Core]
Core --> Out[📊 Actionable Result & Telemetry]
Out --> Agentpython example_usage.pyfrom client import PersonalVaultZeroKnowledgeSanitizer
client = PersonalVaultZeroKnowledgeSanitizer()
result = client.run_privacy_benchmark()
print(result)Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json{
"mcpServers": {
"genpark-personal-vault-zero-knowledge-sanitizer-skill": {
"command": "python",
"args": ["/path/to/genpark-personal-vault-zero-knowledge-sanitizer-skill/mcp_server.py"]
}
}
}python mcp_server.py --testParameter | Type | Required | Description |
|
| Yes | Primary context, text, or task input |
|
| No | Execution flags, thresholds, or sensitivity bounds |
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Detect and redact PII and secrets before text reaches an LLM, with reversible placeholders.
Redact personal data from text before your agent sends it to a model or stores it: names, contact details, ID numbers, financial and health identifiers, card numbers, plus any exact terms you add. Nothing stored, no third-party AI. Tools - redact_text: returns the redacted text and a count of what was removed, by type. Never the removed values. Options: restrict to particular data types, add exact terms to remove, choose the replacement style (a type label such as <PERSON> by default) and set a confidence threshold. - list_entity_types: lists the detectable data types. Free. Data handling - Nothing you send is stored. Text is processed in memory and discarded when the call returns. - No third-party AI. Text is never sent to OpenAI, Anthropic, Google or any other outside model. - Tool arguments are never logged. Only counts are kept: credits used, a timestamp and how many of each type were found. PDFs, including scanned pages, are handled by the REST API with the same key and credits. They aren't offered as an MCP tool because a file doesn't fit in a tool call. Pricing: 1 credit per call plus 1 per 1,000 characters. 10 free credits on sign-up, no card. Credit packs from $5; purchased credits never expire. Failed calls and repeated identical calls are free. Setup and API key: https://eleyed.com/mcp?utm_source=glama&utm_medium=directory&utm_campaign=listing
Find and redact leaked secrets, wallet keys and PII before an AI agent sends text.
Detect and redact PII and secrets (API keys, tokens) in text, HTML or PDF before it reaches an LLM.
