anonymize-mcp
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- AlicenseAqualityAmaintenanceLocal pseudonymisation MCP server that detects PII in text, replaces it with opaque tokens before sending to cloud LLMs, and restores tokens afterward.2199 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
- FlicenseNot gradedqualityCmaintenanceEnables secure text authoring and privacy-focused document processing through MCP. Provides tools for style analysis, conservative text revision, comparison with author samples, and removal of metadata from DOCX/PDF files.-
- FlicenseNot gradedqualityCmaintenanceMCP server for anonymizing and deanonymizing PII through the Pseudora API, enabling safe sharing of sensitive text with AI assistants.-
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.