Strac MCP DLP
OfficialRelated Servers
Alternatives to Strac MCP DLP
No user-submitted related servers found.
Related Servers
- AlicenseAqualityAmaintenanceSanitizes text and files by removing PII, secrets, and custom patterns locally before sending to LLMs, with optional reverse-scrubbing.3507 npm2Cryptographic Autonomy 1.0 (Combined Work Exception)
- AlicenseNot gradedqualityDmaintenanceLocal-first CLI and MCP server for redacting sensitive text before sharing logs, configs, and errors with AI tools.MIT
- AlicenseNot gradedqualityFmaintenanceScans files, directories, and environment configurations for exposed secrets, API keys, and credentials, redacting matches and providing allowlist support. Enables natural-language secret detection in projects and CI/CD integration via CLI.115 npmMIT
- AlicenseAqualityBmaintenanceAnonymizes documents via the Stript desktop app locally, ensuring personal data never enters the conversation. Provides tools to anonymize files and clipboard content, fetch results, and restore original values to local files.510 npmMIT
- AlicenseAqualityCmaintenanceScans diffs, files, and snippets for leaked secrets like AWS keys, GitHub tokens, and private keys, returning redacted findings while running fully locally without network calls.1MIT
- AlicenseNot gradedqualityAmaintenanceScans text and files for common secrets (AWS, GitHub, etc.) and redacts them to prevent credential leakage in AI-assisted development. Runs entirely locally with no telemetry.12 PyPIMIT
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.