Mcp Plesk Dev Docs

io.github.barateza由 GitHub 帳號 barateza 發布v0.6.0更新於 Oct 24, 2026

An MCP server that indexes and retrieves Plesk documentation.

已驗證STDIO僅桌面知識與記憶開發者工具

概覽

AI 產生的概覽

讓助理在本地索引的 Plesk 擴充功能開發文件(指南、API、CLI 與 SDK 參考)中檢索並取得內容。

可以做什麼
將五個 Plesk 文件來源(管理指南、REST API 參考、CLI 參考、PHP SDK、JS SDK)索引到本機向量與全文儲存中,並提供一個統一搜尋工具。工具包括帶重排序的混合搜尋 search_plesk_unified、取得完整文件的 get_file_content、尋找符號引用的 resolve_references、更新索引的 refresh_knowledge 與 trigger_index_sync,以及檢查就緒狀態與延遲的 daemon_health。另提供 API、CLI、指南與 PHP 存根的目錄資源,並在啟用取樣時產生附行內引用的答案。
適用情境
適合在開發 Plesk 擴充功能時,需要跨多套文件查找 API、CLI 或 SDK 細節,而不想逐一手動搜尋的情況。它是文件檢索工具,不能用來管理線上 Plesk 伺服器。
執行需求
透過 stdio 在本機執行,可從 PyPI 安裝(例如使用 uvx)或以 Python 工具從原始碼安裝。使用前需先執行初始索引。選用環境變數可設定模型等級、取樣、預熱、摘要,以及用於 AI 合成答案的 OPENROUTER_API_KEY。針對 Apple Silicon 最佳化;記憶體用量隨模型等級增加。
安裝前請注意
選用的 OPENROUTER_API_KEY 屬於憑證,應妥善保管;啟用取樣會把搜尋結果傳送到該外部服務。refresh_knowledge 與 trigger_index_sync 會重新抓取來源並改寫本機索引。本專案為個人作品,與 Plesk 無隸屬或背書關係,部分 Plesk 所屬檔案的授權狀態不明確。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Mcp Plesk Dev Docs,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

mcp-plesk-dev-docs

[Python 3.12+] [PyPI Version] [PyPI Downloads] [MCP Registry] [License: MIT] [MCP Compatible] [Code style: black] [Ruff] [MCP Badge]

[!NOTE] This MCP server provides unified documentation search for extension developers. If you are looking to manage your live Plesk server via AI, please see the official Plesk MCP Server.

State-of-the-Art (SOTA) semantic search across the entire Plesk documentation surface, optimized for sub-second latency on Apple Silicon.


Why this exists

Plesk documentation is spread across five separate sources: an admin guide, a REST API reference, a CLI reference, a PHP SDK, and a JS SDK. Answering a single extension development question often means searching all of them manually, cross-referencing results, and still missing the relevant section.

This server ingests all five sources, embeds them with a multilingual model, and exposes a single search_plesk_unified MCP tool. It uses hybrid search (Vector + FTS), Reciprocal Rank Fusion (RRF), and Cross-Encoder reranking to deliver high-precision results in milliseconds.


Architecture & Performance

mermaid
flowchart TD    Client["MCP Client\n(Claude Desktop / Cursor / etc.)"]
    Client -->|"search_plesk_unified(query)"| Server
    subgraph Server["FastMCP Server · Modular Architecture"]        direction TB        Main["Bootstrap · server/main.py"]        Life["Lifecycle Hooks · server/lifecycle.py"]        Tools["MCP Tools · server/mcp_app.py"]
        Main --> Life --> Tools    end
    subgraph Pipeline["Retrieval Pipeline"]        direction TB        E["1 · Embed query\n(Hardware-accelerated)"]        S["2 · Hybrid Search\nVector (LanceDB) + FTS (Tantivy)"]        R["3 · RRF Merge + Rerank\n(MiniLM-L4-v2)"]        N["4 · Neighbor Expansion\n(Context Enrichment)"]        A["5 · AI Synthesis\n(sampling-enabled)"]        E --> S --> R --> N --> A    end
    subgraph Store["LanceDB Vector & FTS Store"]        direction LR        G["Guide"]        A_["API"]        C["CLI"]        P["PHP Stubs"]        J["JS SDK"]    end
    Tools --> Pipeline    S <--> Store

Performance Benchmarks (2026-05-04)

Optimized for Apple Silicon (M2/M3) using MPS acceleration and memory-resident table caching.

ProfileEmbed ModelHR@5MRR@5Avg LatencyEst. RAM
lightBAAI/bge-small100.0%0.9171.007 s~200 MB
mediumBAAI/bge-base100.0%0.917~0.60s~600 MB
full-tqBAAI/bge-m375.0%0.750~0.40s~1300 MB

Metrics measured on Apple M2 Pro with LanceDB connection caching enabled.


Key Features

  • Single-Instance Lock: PID-based lock prevents concurrent LanceDB access when multiple MCP clients or IDE sessions try to launch the server simultaneously.
  • Sub-Second Hybrid Search: Combined Vector + Tantivy FTS with RAM-cached table connections for instant retrieval.
  • AST-Aware Chunking: Uses tree-sitter to respect class and method boundaries in PHP, JS, and TS documentation.
  • TurboQuant Acceleration: Fast 4-bit quantized search for the full-tq profile, delivering 10x lower latency for large models.
  • Neighborhood Retrieval: Automatically fetches adjacent chunks (prev/next) to provide complete context for grounding.
  • Macro-Context Summaries: Injects file-level purpose summaries into every chunk using the SummaryCache.
  • AI-Synthesized Answers: Generates concise answers from search results with structured inline citations [1], [2].

MCP Components

This server provides tools, prompts, and resources. See docs/mcp-components.md for a full reference.

Primary Tools

ToolDescription
search_plesk_unifiedHybrid search with RRF and Cross-Encoder reranking.
get_file_contentRetrieve the full content of a specific documentation file.
resolve_referencesFind all files referencing a specific symbol or topic.
refresh_knowledgeRe-fetch sources and update the index (incremental).
trigger_index_syncStart a background indexing job.
daemon_healthCheck readiness, hardware acceleration (MPS/CUDA), and latency stats.

Resources

  • plesk://toc/api - Table of Contents for API documentation.
  • plesk://toc/cli - Table of Contents for CLI reference.
  • plesk://toc/guide - Table of Contents for Extensions Guide.
  • plesk://toc/php-stubs - Hierarchical list of PHP classes.

🚀 Installation & Setup

Because this server is published to PyPI and listed on the MCP Registry, you don't even need to clone the repository to run it!

Option 1: Run instantly via uvx (Recommended)

You can run or integrate the server in seconds.

1. Add to Claude Desktop

Add the server config to your claude_desktop_config.json (typically at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS, or %APPDATA%\Claude\claude_desktop_config.json on Windows):

json
{  "mcpServers": {    "plesk-dev-docs": {      "command": "uvx",      "args": ["mcp-plesk-dev-docs"]    }  }}
2. Configure in Cursor

Go to Settings > Features > MCP, click + Add New MCP Server:

  • Name: plesk-dev-docs
  • Type: command
  • Command: uvx mcp-plesk-dev-docs

Option 2: Local Developer Setup (Manual Build)

If you want to modify the source code, run benchmarks, or manage database migrations:

Quick bootstrap (recommended):

bash
git clone https://github.com/barateza/mcp-plesk-dev-docs.gitcd mcp-plesk-dev-docs./install.sh          # Linux / macOS# powershell -ExecutionPolicy Bypass -File install.ps1   # Windows

Manual setup:

bash
git clone https://github.com/barateza/mcp-plesk-dev-docs.gitcd mcp-plesk-dev-docsuv pip install -e ".[dev]"
  1. Run Initial Indexing: Generate the offline vector database and full-text search indexes:

    bash
    uv run python -m mcp_plesk_dev_docs.server.main refresh_knowledge
  2. Start the Server:

    bash
    uv run python -m mcp_plesk_dev_docs.server.main

Configuration

Set environment variables in .env:

env
PLESK_MODEL_PROFILE=light       # light | medium | full-tqPLESK_ENABLE_SAMPLING=true     # AI-Synthesized answersPLESK_DAEMON_AUTO_WARMUP=true  # Preload models on startupPLESK_INDEX_SUMMARIES=true     # Enable file-level summariesOPENROUTER_API_KEY=sk-or-v1-...

Documentation


License

MIT. See LICENSE.

Ownership & Disclaimer

This is a personal project by Gilson Siqueira. It is not officially affiliated with, endorsed by, or supported by Plesk or WebPros International GmbH. Plesk is a trademark of WebPros International GmbH.

Important notice about Plesk-owned deliverables

Portions of this repository were developed under contract for Plesk International GmbH ("Plesk") only if specifically identified as such. The MIT license above applies only to material the repository owner is authorized to license. Files or directories owned by Plesk, if any, are listed in NOTICE. If you need assurance about licensing for a particular file, contact Plesk or seek legal counsel before relying on the MIT License for Plesk-owned files.


Built to make Plesk extension development faster.

來源:README.md,提交 246bce8

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版本歷史

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  1. v0.6.0最新Sep 16, 2026

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