Plesk Dev Docs MCP server

mcp-plesk-dev-docs

[!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 searchpleskunified 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

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.

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

Key Features

Shortened. The full README is on GitHub.