anonymize-mcp
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- AlicenseNot gradedqualityCmaintenanceMCP server for Intelation Nexis. It lets Claude, Cursor, VS Code and other assistants detect and remove names, emails, phone numbers, NHS and SSN numbers from text. Methods include redact, replace, mask, hash, pseudonymize, tokenize and encrypt. Tools: anonymize_text, detect_pii, list_countries, plus an optional decrypt_text that is off by default. Needs a free API key from nexis.intelation.com.MIT
- AlicenseAqualityBmaintenanceLocal pseudonymisation MCP server that detects PII in text, replaces it with opaque tokens before sending to cloud LLMs, and restores tokens afterward.2140 npm2MIT
- FlicenseNot gradedqualityDmaintenanceMCP server for automatic detection and redaction of PII in text, with anonymization and deanonymization capabilities, all local processing.1-
- AlicenseAqualityCmaintenanceProvides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.7MIT
- AlicenseAqualityDmaintenanceProvides local anonymization of Czech legal documents by replacing sensitive entities with pseudonyms to ensure privacy during LLM interactions. It allows users to safely process documents like contracts and judgments by keeping original data offline and facilitating local deanonymization.55MIT
- 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 6 tools
Each tool has a unique, well-defined purpose: morphological analysis, anonymization, readability checking, text correction, entity extraction, and translation. There is no overlap in functionality, and descriptions clearly differentiate them.
Most tools follow a verb_noun pattern (analyze_morphology, check_readability, correct_text, extract_entities, translate_text), while 'anonymize' is a standalone verb. This minor inconsistency does not hinder understanding.
With 6 tools, the server covers essential NLP tasks for Czech legal texts without being over- or under-scoped. Each tool earns its place.
The toolset provides a comprehensive pipeline for processing legal texts (analysis, correction, anonymization, translation, entity extraction, readability). Minor gaps like summarization exist, but core workflows are well covered.