adeu: docx LLM translator

by dealfluence · MCP Server · ★ 168

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install adeu

npx -y @smithery/cli install adeu --client claude
uvx adeu extract contract.docx -o contract.md

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 67/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsdocument-automationdocx-convertergemini-cli-extensionlegaltechllm-toolsmcp-serverpython-libraryredline

Quick Facts

Stars168
Forks26
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score67/100
Last Updated2026-10-16
Created2025-12-30
Platformscli, gemini, mcp, python
Est. Tokens~16k

About adeu

Adeu: Track Changes for the LLM era LLMs speak Markdown; reviewers speak "Track Changes." Adeu is a docx ↔ LLM translator: a Model Context Protocol (MCP) server (Python and Node.js implementations) and accompanying SDKs that act as a Virtual DOM for Microsoft Word.

Compatible Skills

These tools work well together with adeu for enhanced workflows:

  • word-mcp-live — semantic(0.56)+rare_topics+same_lang+similar_pop+shared_platform (54%)
  • gongwen-format-skill — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)
  • codebase_to_text — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (52%)

adeu alternative? Top 6 similar tools

Looking for a adeu alternative? If you're comparing adeu with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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