**Give Your AI Assistant는 Gutenberg HTML을 추측하는 대신 검증된 블록 마크 업 데이터베이스입니다. **

wp-blockmarkup-mcp는 WordPress 코어, WooCommerce 또는 모든 블록 기반 플러그인에서 추출, 검증 및 인덱스를 제공하는 로컬 MCP 서버입니다. Claude Code와 같은 AI 도구는 블록 스키마, 속성 및 검증된 Markup 예제의 검증된 데이터베이스를 쿼리합니다. 대신 블록 구조, 발명 속성을 수집하는 교육 데이터에 의존하고 편집기에서 "Attempt Block Recovery"를 트리거하는 마크 업을 생성합니다.

**현재 유효성 검사 결과 : ** 121 코어 Gutenberg 블록 색인 - 정적 블록 마크 업 예의 100 %는 구조 및 저장 기능 검증을 모두 통과합니다.

왜 이 존재

AI 조수는 점점 WordPress 콘텐츠 프로그래밍을 생성하기 위해 사용되었습니다. REST API를 통해 페이지를 구축하고 플랫폼 간의 콘텐츠를 마이그레이션하고 헤드리스 프론트 엔드를 팝업. 그러나 모든 AI 모델은 동일한 블라인드 스폿을 공유합니다. ** 블록 마크 업은 실제 블록 소스 코드**에서 훈련 데이터에서 제공됩니다.

이 수단:

• ** 모델은 존재하지 않는 속성을 발명 ** — fontSize] 문자열 슬러그를 기대할 때 숫자로 "large"

wp-blockmarkup-mcp

Give your AI assistant a verified block markup database instead of letting it guess Gutenberg HTML.

wp-blockmarkup-mcp is a local MCP server that extracts, validates, and indexes every Gutenberg block from WordPress core, WooCommerce, or any block-based plugin you work with. It gives AI tools like Claude Code a verified database of block schemas, attributes, and validated markup examples to query — instead of relying on training data that hallucinates block structures, invents attributes, and produces markup that triggers “Attempt Block Recovery” in the editor.

Current validation results: 121 core Gutenberg blocks indexed — 100% of static block markup examples pass both structural and save-function validation.

Why This Exists

AI assistants are increasingly used to generate WordPress content programmatically — building pages via the REST API, migrating content between platforms, populating headless frontends. But every AI model shares the same blind spot: block markup comes from training data, not from the actual block source code.

This means:

• Models invent attributes that don’t exist — fontSize as a number when it expects a string slug like "large"
• Class naming patterns are wrong — has-background-color-vivid-red instead of has-vivid-red-background-color
• Block nesting rules are ignored — putting blocks inside containers that don’t support InnerBlocks
• Dynamic blocks get static markup — generating HTML for blocks that only accept comment delimiters with attributes
• Third-party blocks are invisible — WooCommerce blocks, Kadence blocks, ACF blocks are all unknown to training data
• Subtle mismatches break silently — the markup looks right but the editor flags it as invalid, requiring manual recovery

You only discover these problems when content lands in the editor and every block shows a yellow warning bar. In an agentic workflow where the AI generates and pushes content autonomously, bad markup means broken pages.

The Solution

Feed the AI real block data. Instead of hoping the model remembers the right markup format, give it a verified database to query and validate against.

wp-blockmarkup-mcp parses the actual source code of any block-based plugin, extracts every block’s schema, and validates generated markup through a two-tier pipeline — first using the official WordPress block parser for structural correctness, then verifying HTML output patterns against the block’s save() function AST. Your AI assistant queries this database before generating content, and can validate its output before pushing it.

This fits naturally into content generation workflows. When Claude Code (or any MCP-compatible assistant) needs to generate Gutenberg block markup, it: