Devdocs Mcp

io.github.madhan-g-p由 GitHub 账号 madhan-g-p 发布v1.0.0更新于 Oct 24, 2026

Local DevDocs MCP Server (Developer Docs with project specific context awareness)

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

一个本地 MCP 服务器,为 AI 助手提供与项目版本匹配、离线缓存的 DevDocs 文档。

可以做什么
DevDocs-MCP 在本地运行,充当 AI 编程助手与 DevDocs.io 数据集之间的文档层。它会按你指定的依赖项抓取文档、缓存到磁盘,并对缓存内容提供排序模糊搜索。它会把文档映射到项目中声明的版本,使回答对应你实际使用的库版本,而不是训练数据。它不是智能体、代码生成器或网页爬虫。
适用场景
当助手总是猜错 API 签名或混淆库版本,且你希望文档查询无需网络延迟时适用。最适合使用 DevDocs 上已有文档的常见 JavaScript 与 TypeScript 技术栈的开发者。
运行要求
作为本地 Node.js 进程运行(需要 Node.js 18+,已在 18.x 和 20.x 测试)。可通过 npm 包 @madhan-g-p/devdocs-mcp-server 安装,或用 pnpm 从源码构建。配置使用 .env 文件中的 DEVDOCS_DATA_PATH、MCP_DB_PATH 和 LOG_LEVEL。仅在首次抓取文档时需要网络,之后可离线使用。Docker 为可选方式。
安装前请注意
项目自称处于活跃且密集开发阶段,部分功能尚未测试,API 预计会变动,可能不稳定。抓取文档会从 DevDocs.io 下载数据集,需要网络和磁盘空间。它会向你配置的路径写入缓存文件和 SQLite 数据库,请将 DEVDOCS_DATA_PATH 与 MCP_DB_PATH 指向你愿意被修改的位置。不涉及凭据或付费。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Devdocs Mcp,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

DevDocs-MCP: Documentation Authority for AI Agents

Eliminate AI hallucinations with local, version-aware, and authoritative documentation.

DevDocs-MCP is a Model Context Protocol (MCP) server that provides version-pinned, deterministic documentation sourced from DevDocs.io to AI assistants (Claude, RooCode, Cline, Copilot etc.). It acts as a local Documentation Intelligence Layer, ensuring your agent always has the correct API context without network latency or training data drift.

This server follows the proposed MCP server standard defined by the MCP , which is being adopted by AI tools and agents across the ecosystem, including those from providers such as Anthropic, OpenAI, and Google.


⚠️ Project Status Notice ⚠️ This project is under active and heavy development. Still need to test existing features and implement more new features like optimal caching , update latest doc automatically and also on request basis , optimal querying and more... Design decisions, APIs, and structure are expected to evolve based on strong opinions, reviews, and feedback from the developer community. I warmly welcome discussions, critiques, and contributions to help shape this project in the right direction.


📑 Table of Contents

  1. Overview
  2. Key Capabilities
  3. Quickstart
  4. Installation & Setup
  5. AI Agent Configuration
  6. Further Reading

🎯 Overview

What This Project Is
  • A local MCP server written in NestJS (TypeScript).
  • A deterministic authority for documentation metadata and content.
  • A lazy-ingestion engine that caches DevDocs offline.
  • A project-aware context manager that pins docs to your package.json versions.
What This Project Is NOT
  • ❌ An AI agent or code generator.
  • ❌ A web scraper (it uses structured DevDocs datasets).
  • ❌ A DevDocs UI replacement.
  • ❌ A hosted SaaS (it runs 100% locally).
Why This Exists (The Problem)

Modern AI coding agents face three major hurdles:

  1. Hallucination: Guessing API signatures or using deprecated methods.
  2. Latency: Fetching live web docs for every query is slow.
  3. Version Drift: Mixing up React 18 with React 19 features.

The Solution: DevDocs-MCP provides "Just-in-Time" documentation retrieved from local storage, filtered by your project's specific dependency versions.

Mental Model

DevDocs-MCP acts as a middleware between your IDE Agent and the documentation source.

mermaid
graph TD    Agent["AI Agents"] -->|MCP Search/Explain| Server["DevDocs-MCP Server"]    Server -->|Metadata| DB[(SQLite - sql.js)]    Server -->|Content| FS[Local Disk - JSON Cache]    Server -.->|Lazy Fetch| Web[DevDocs.io API]

🚀 Key Capabilities

  • Offline-First: Documentation is cached locally; no internet is needed after ingestion.
  • Ranked Fuzzy Search: Instantly find relevant entries (e.g., "intersection observer").
  • Version Awareness: Automatically maps to specific library versions in your project.
  • Node-Only Architecture: Uses sql.js for zero-native dependencies (no Python/C++ build steps).
  • Structured Outputs: Returns clean, LLM-optimized content.

⚡ Quickstart

  1. Clone & Install
bash
git clone https://github.com/madhan-g-p/DevDocs-MCP.gitcd DevDocs-MCPpnpm install
  1. Configure Environment
bash
cp .env.example .env# Edit .env: Set DEVDOCS_DATA_PATH to where you want to store docs.
  1. Build & Run
bash
pnpm buildpnpm start:prod

Note: Strictly use pnpm for development and contributions.


🛠️ Installation & Setup

DevDocs-MCP is optimized for zero-fuss setup with pnpm. It is a Node-only project.

Prerequisites

  • Node.js 18+ (tested on 18.x and 20.x).

🐳 Docker Usage

DevDocs-MCP is designed to be lightweight and persistent. Since it downloads and caches large documentation datasets, managing your data volumes correctly is essential.

Method A: Docker Compose (Dynamic & Persistent)

This is the recommended way. It supports 3 storage modes via the DEVDOCS_VOLUME_SOURCE variable in your .env file.

  1. Configure your preferred storage in .env:

    bash
    # Mode 1: Named volume (Default - Docker manages it)DEVDOCS_VOLUME_SOURCE=devdocs_data
    # Mode 2: Host Path (Outside Docker - Local folder)# DEVDOCS_VOLUME_SOURCE=./my-docs-storage
  2. Start the server:

    bash
    docker compose up -d

Flexibility: Every time you start the container, you can point to a new volume (to start fresh) or an existing volume (to keep your data) just by changing the DEVDOCS_VOLUME_SOURCE in your .env file. Both the mcp.db (database library) and the data/ (documentation downloads) are stored inside this volume.

Method B: Docker Run (Manual)

If you just want to run the public image directly from Docker Hub:

bash
docker run -d -p 3000:3000 \  -v "devdocs_data:/app/data" \  --name devdocs-mcp \  madhandock1/devdocs-mcp:latest
💾 Data Strategy

The project uses a single mount point at /app/data which contains:

  • /app/data/mcp.db: The SQLite registry.
  • /app/data/docs/: The cached documentation files. This unified structure makes it easy to backup or move your entire documentation authority by simply moving one folder or volume.

Configuration (.env)

VariableDescriptionDefault
DEVDOCS_DATA_PATHPath to store downloaded documentation JSONs../data
MCP_DB_PATHPath to the SQLite metadata database.mcp.db
LOG_LEVELVerbosity (debug, info, warn, error).info

Ingesting Documentation

Once the server is connected to your agent, use the ingest tool to download documentation for your specific stack: Agent will take care of it once the server is up and running , it is supposed to list the available to tools and take decision accordingly.

json
{  "dependencies": {    "react": "18.2.0",    "typescript": "5.0.0"  }}

Note: The Devdocs fetch url required to be used for ingestion is defined in src/config/constants.ts. This URL currently points to the correct and officially supported path. If the devdocs community changes this path in future, the configuration may need to be updated accordingly.
This approach does not involve web scraping. It follows the same supported mechanism used by DevDocs to provide documentation for offline use, similar to how users download documentation within DevDocs itself.


🤖 AI Agent Configuration

RooCode / Cline / Claude Desktop (Recommended: STDIO)

Add this to your mcp_settings.json. By setting the cwd (Current Working Directory), the server will automatically load configuration from your .env file, allowing you to use relative paths for data storage.

json
{  "mcpServers": {    "devdocs": {      "command": "node",      "args": ["dist/main.js"],      "cwd": "file://path/to/your/DevDocs-MCP"    }  }}

Pro Tip: Use the absolute path to the project in cwd. The server will then find your .env and use the DEVDOCS_DATA_PATH and MCP_DB_PATH you've defined there (e.g., ./data or mcp.db), making the setup portable!

Remote / Web Clients (HTTP SSE)

If you need to expose the server via a port (for production/remote setups):

  1. Start the server with a port flag:
    bash
    node dist/main.js --port 3000
  2. Configure your client:
    • SSE URL: http://your-server:3000/mcp/sse
    • Type: SSE (Supported by most MCP clients, atleast I guess so)

📖 Further Reading


Inspired from this project devdocs-local , devdocs-local-demo by @Nathaniel Whiteinge

来源:README.md,提交 ef64941

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v1.0.0最新Sep 16, 2026

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