대만 교육부(MoE)의 어휘, 구두점, 문자 형태 기준을 집행하는 번체 중국어(zh-TW)의 언어 린터입니다. 모델 컨텍스트 프로토콜(MCP)을 통해 AI 코딩 보조원에 연결되고, 사용자가 도달하기 전에 본토 중국어(zh-CN) 지역별 이동을 포착합니다.

이 도구는 세 가지 공식 대만 표준을 시행합니다:

• 개정 구두점 안내서 (《重訂標點符號手冊》修訂版) – 구두점
• 국민 문자 표준형(《國字標準字體》) – 문자 모양
• OpenCC의 TWPhrases/TWVariants 데이터셋에 기반한 해협 횡단 어휘 정규화 – 단어 선택

1100개 이상의 어휘 규칙과 15개의 케이싱 규칙이 이진수로 컴파일됩니다. 모호한 용어의 경우, 서버는 내부 AI 어시스턴트에게 도움을 요청하며 추가 API 키는 필요하지 않습니다.

왜 이런 게 존재하는가

현대 중국어는 충분히 표준화되지 않은 언어입니다

청나라 말기에는 학자들이 서양 개념을 모국어휘 없이 문자 체계로 표현해야 했다. 새로운 단어를 만들거나 일본어(和製漢語)를 통해 번역을 가져오는 등 엄청난 시간 압박 속에서 문학 체계를 구축했다.

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: