Strac MCP DLP
OfficialRelated Servers
Alternatives to Strac MCP DLP
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Related Servers
- AlicenseBqualityBmaintenanceZero-Trust local PII & secrets masking MCP (ZTDS RFC v1.0). Zero server logs. Features: 23 zero-trust tools, 30 industry profiles, offline OCR, RAM-only masking. Run: npx -y @privacyscrubber/mcp-server. Website: https://privacyscrubber.com SDK: https://www.npmjs.com/package/@privacyscrubber/sdk423687 npm3MIT

Clotr Guardofficial
AlicenseNot gradedqualityAmaintenanceLets your assistant check text for credentials and personal details before passing it to another tool. Runs on your device over stdio and makes no network calls.38AGPL 3.0- AlicenseNot gradedqualityNot gradedmaintenanceEnables detection and deterministic masking of personally identifiable information, financial account numbers, JWT tokens, and cryptographic keys so sensitive data can be sanitized before further processing. Runs as a zero-dependency Python MCP server over JSON-RPC 2.0 stdio for use in Claude Desktop, Cursor, and other MCP-compatible clients.MIT
- AlicenseAqualityAmaintenanceEnables agents to detect and redact PII and secrets in text, HTML, and PDF documents before they reach an LLM, with reversible placeholder, mask, or hash redaction, and to list supported detectors and redaction styles.4MIT
- AlicenseNot gradedqualityDmaintenanceLocal-first CLI and MCP server for redacting sensitive text before sharing logs, configs, and errors with AI tools.MIT
- AlicenseNot gradedqualityBmaintenanceEnables agents and applications to scan text streams for personally identifiable information, financial account numbers, JWT tokens, and cryptographic keys, returning sanitized output with deterministic zero-leakage masking. It runs as a zero-dependency MCP stdio server or importable Python module so regulatory compliance policies (GDPR/HIPAA-style) can be enforced on data before it leaves a workflow.7MIT
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: detect vs. redact, text vs. file, and detokenize for vault resolution. The detect/redact pair on text and file is well-differentiated by descriptions and output behavior.
Most tools follow a readable verb_noun pattern like redact_text, detect_file, and redact_file. detect_sensitive_data fits the pattern too, though it names the data type rather than the input kind, and detokenize is a single verb rather than verb_noun.
With five tools, the server is tightly scoped without being thin. Each tool covers a distinct operation that earns its place in the DLP workflow.
The set covers detect and redact for both text and files, plus detokenization for authorized recovery. A minor gap is that token creation is not explicitly surfaced as its own tool, but redaction likely handles that internally, so core workflows are covered.