Mcp Plesk Dev Docs

io.github.barateza由 GitHub 账号 barateza 发布v0.6.0更新于 Oct 24, 2026

An MCP server that indexes and retrieves Plesk documentation.

已验证STDIO仅桌面知识与记忆开发者工具

概览

AI 生成的概览

让助手在本地索引的 Plesk 扩展开发文档(指南、API、CLI 与 SDK 参考)中进行检索和获取内容。

可以做什么
将五个 Plesk 文档来源(管理指南、REST API 参考、CLI 参考、PHP SDK、JS SDK)索引到本地向量与全文存储中,并提供一个统一搜索工具。工具包括带重排序的混合搜索 search_plesk_unified、获取完整文档的 get_file_content、查找符号引用的 resolve_references、更新索引的 refresh_knowledge 与 trigger_index_sync,以及检查就绪状态和延迟的 daemon_health。还提供 API、CLI、指南和 PHP 存根的目录资源,并在启用采样时生成带行内引用的答案。
适用场景
适合在开发 Plesk 扩展时,需要跨多套文档查找 API、CLI 或 SDK 细节,而不想逐个手动搜索的场景。它是文档检索工具,不能用于管理线上 Plesk 服务器。
运行要求
通过 stdio 在本地运行,可从 PyPI 安装(例如使用 uvx)或用 Python 工具从源码安装。使用前需先执行初始索引。可选环境变量用于配置模型档位、采样、预热、摘要,以及用于 AI 合成答案的 OPENROUTER_API_KEY。针对 Apple Silicon 优化;内存占用随模型档位增加。
安装前请注意
可选的 OPENROUTER_API_KEY 属于凭据,应妥善保管;启用采样会把搜索结果发送到该外部服务。refresh_knowledge 和 trigger_index_sync 会重新抓取来源并重写本地索引。该项目为个人作品,与 Plesk 无隶属或背书关系,部分 Plesk 所属文件的许可状态不明确。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Mcp Plesk Dev Docs,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

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.

来源:README.md,提交 246bce8

工具

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版本历史

1
  1. v0.6.0最新Sep 16, 2026

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