繁体字中国語(zh-TW)の言語リンターで、台湾教育部(MoE)の語彙、句読点、文字形の基準を適用します。モデルコンテキストプロトコル(MCP)を通じてAIコーディングアシスタントに接続し、中国本土(zh-CN)の地域変動をユーザーに到達する前に捉えます。

このツールは台湾の3つの公式基準を強制します。

• 改訂標点符号手冊(Revised Handbook of Punctuation)-- 句読点
• 国字標準形(《國字標準字體》)-字形
• OpenCCのTWPhrases/TWVariantsデータセットに基づく海峡横断語彙正規化 -語選択

1100以上の語彙ルールと15のケースングルールがバイナリにコンパイルされています。曖昧な用語については、サーバーが内部のAIアシスタントに判断の助けを求めます。追加のAPIキーは必要ありません。

なぜこれが存在するのか

現代中国語は十分に標準化されていない言語です

清代末期、学者たちは西洋の概念を母語の語彙がない文字体系で表現しなければならなかった。新語を造るにせよ、和製漢語(和製漢語)を通じて翻訳を輸入するにせよ、彼らは莫大な時間的制約の中で文学体系を組み立てた。

zhtw-mcp

A linguistic linter for Traditional Chinese (zh-TW) that enforces Taiwan Ministry of Education (MoE) standards on vocabulary, punctuation, and character shapes. It plugs into AI coding assistants through the Model Context Protocol (MCP) and catches Mainland Chinese (zh-CN) regional drift before it reaches the user.

The tool enforces three official Taiwan standards:

• Revised Handbook of Punctuation (《重訂標點符號手冊》修訂版) – punctuation marks
• Standard Form of National Characters (《國字標準字體》) – character shapes
• Cross-strait vocabulary normalization, grounded in OpenCC’s TWPhrases/TWVariants datasets – word choices

Over 1100 vocabulary rules and 15 casing rules are compiled into the binary. For ambiguous terms, the server asks the AI assistant it runs inside for help deciding – no extra API keys required.

Why this exists

Modern Chinese is an inadequately standardized language

In the late Qing dynasty, scholars had to express Western concepts in a writing system with no native vocabulary for them. Whether coining new words or importing translations via Japanese (和製漢語), they assembled a literary system under enormous time pressure. Many translated terms were inconsistent, ambiguous, or contradictory. The Chinese-speaking world has lived with these deficiencies for over a century.

Simplified Chinese made it worse

The PRC simplification effort reduced not just stroke counts but vocabulary precision. Terms that should vary by domain got flattened into single catch-all translations. Many PRC translations were coined hastily: if a term worked in one context, it spread uncritically to others.

AI models amplify the problem

AI language models learn from web text where Simplified Chinese vastly outweighs Traditional Chinese (roughly 2.6:1 in CC-100). Major datasets like CulturaX do not even track Traditional Chinese separately. A FAccT 2025 study confirmed that most models favor zh-CN terminology when asked to write zh-TW. The output looks plausible but is not how people in Taiwan actually write.

This goes beyond character conversion. The same word often means different things across the strait: