** AI 코딩 조수가 검증 된 WordPress 후크 데이터베이스를 추측 할 때. **

wp-devdocs-mcp는 모든 행동, 필터, 블록 등록 및 WordPress, WooCommerce, Gutenberg, 또는 어떤 플러그인에서 JS API 호출을 색인하는 로컬 MCP 서버입니다. Claude Code와 같은 AI 도구를 제공하여 교육 데이터에 의존하는 대신 검색 및 검증 된 데이터베이스를 제공합니다.

Claude Code, Cursor, Windsurf 및 MCP 호환 클라이언트와 함께 작동합니다.

왜 이 존재

AI 코딩 조수는 WordPress 플러그인을 구축하는 방법을 변경합니다. Claude Code, Cursor 및 Windsurf와 같은 도구는 몇 분 동안 전체 플러그인을 비계 할 수 있습니다. 그러나 그들은 모두 동일한 블라인드 스팟을 공유합니다. ** 실제 소스 코드**에서 훈련 데이터에서 온 후크 이름.

이 시대의 대부분은 잘 작동합니다. Claude Sonnet 못 일반적인 걸이 같이 모형 거의 매번. 그러나 "최고의 시간"은 배송 생산 코드가 아니라 모든 모델은 Claude Sonnet입니다. LLMs의 풍경을 가로지르는 코드 생성 오늘:

• **가 존재하지 않는 모델 인벤트 후크 ** - woocommerce_email_after_order_details 소리가 오른쪽이지만 실제하지 않습니다.
• Some use deprecated namespaces — `wp.

wp-devdocs-mcp

Give your AI coding assistant a verified WordPress hook database instead of letting it guess.

wp-devdocs-mcp is a local MCP server that indexes every action, filter, block registration, and JS API call from WordPress, WooCommerce, Gutenberg, or any plugin you work with. It gives AI tools like Claude Code a verified database to search and validate against instead of relying on training data.

Works with Claude Code, Cursor, Windsurf, and any MCP-compatible client.

Why This Exists

AI coding assistants are changing how we build WordPress plugins. Tools like Claude Code, Cursor, and Windsurf can scaffold entire plugins in minutes — but they all share the same blind spot: hook names come from training data, not from the actual source code.

Most of the time this works fine. Models like Claude Sonnet nail common hooks almost every time. But “most of the time” isn’t good enough when you’re shipping production code, and not every model is Claude Sonnet. Across the landscape of LLMs doing code generation today:

• Some models invent hooks that don’t exist — woocommerce_email_after_order_details sounds right but isn’t real
• Some use deprecated namespaces — wp.editor.InspectorControls instead of wp.blockEditor.InspectorControls
• Most miss newer hooks — suggesting pre_get_posts for WooCommerce order queries when HPOS uses woocommerce_order_list_table_prepare_items_query_args
• Parameters get mixed up — wrong argument count or order in callback signatures
• No model knows your custom plugins — private or proprietary hooks are invisible to every model’s training data

The core issue is simple: we can’t rely 100% on any model to produce correct WordPress hook names from memory alone. Even the best models benefit from verification, and the rest genuinely need it. You only find out about hallucinated hooks when the code doesn’t work — and in an agentic workflow where the AI writes, tests, and iterates autonomously, one bad hook name can send it down a rabbit hole of debugging something that was never going to work.

The Solution

Feed the LLM real data. Instead of hoping the model remembers the right hook name, give it a verified database to query.

wp-devdocs-mcp parses the actual source code of any WordPress plugin and builds a searchable index of every hook with its exact name, type, parameters, file location, and surrounding code context. Your AI assistant queries this index before writing code — so every hook name in the generated code is verified against the real source.

This fits naturally into agentic coding workflows. When Claude Code (or any MCP-compatible assistant) needs to use a WordPress hook, it:

1. Searches the indexed database for relevant hooks