search-webdeveloper-tools

Documentation Scraper▌

by arabold

Easily retrieve swift language documentation from GitHub, NPM, PyPI, and web pages with accurate, up-to-date references

Provides specialized documentation scraping and retrieval from GitHub, NPM, PyPI, and web pages, enabling accurate reference to up-to-date library documentation without disrupting workflow.

github stars

★ 1.1K

GitHub npm

Runs locally — no data leaves your networkWeb UI for managing documentation sourcesVersion-specific documentation targeting

best for

/ Developers wanting AI assistants with current library knowledge
/ Teams needing accurate documentation references in AI workflows
/ Anyone tired of AI hallucinations about API details

capabilities

/ Scrape documentation from GitHub repositories
/ Index NPM and PyPI package documentation
/ Process HTML, Markdown, PDF, and Office documents
/ Query version-specific library documentation
/ Index local documentation folders
/ Search across multiple documentation sources

what it does

Fetches and indexes official documentation from GitHub, NPM, PyPI, and web sources so AI assistants can reference current, accurate library docs instead of hallucinating outdated information.

about

Documentation Scraper is a community-built MCP server published by arabold that provides AI assistants with tools and capabilities via the Model Context Protocol. Easily retrieve swift language documentation from GitHub, NPM, PyPI, and web pages with accurate, up-to-date references It is categorized under search web, developer tools.

how to install

You can install Documentation Scraper in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Documentation Scraper is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Grounded Docs: Your AI's Up-to-Date Documentation Expert

Docs MCP Server solves the problem of AI hallucinations and outdated knowledge by providing a personal, always-current documentation index for your AI coding assistant. It fetches official docs from websites, GitHub, npm, PyPI, and local files, allowing your AI to query the exact version you are using.

✨ Why Grounded Docs MCP Server?

The open-source alternative to Context7, Nia, and Ref.Tools.

✅ Up-to-Date Context: Fetches documentation directly from official sources on demand.
🎯 Version-Specific: Queries target the exact library versions in your project.
💡 Reduces Hallucinations: Grounds LLMs in real documentation.
🔒 Private & Local: Runs entirely on your machine; your code never leaves your network.
🧩 Broad Compatibility: Works with any MCP-compatible client (Claude, Cline, etc.).
📁 Multiple Sources: Index websites, GitHub repositories, local folders, and zip archives.
📄 Rich File Support: Processes HTML, Markdown, PDF, Word (.docx), Excel, PowerPoint, and source code.

🚀 Quick Start

1. Start the server (requires Node.js 22+):

npx @arabold/docs-mcp-server@latest

2. Open the Web UI at http://localhost:6280 to add documentation.

3. Connect your AI client by adding this to your MCP settings (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "docs-mcp-server": {
      "type": "sse",
      "url": "http://localhost:6280/sse"
    }
  }
}

See Connecting Clients for VS Code (Cline, Roo) and other setup options.

<details> <summary>Alternative: Run with Docker</summary>

docker run --rm \
  -v docs-mcp-data:/data \
  -v docs-mcp-config:/config \
  -p 6280:6280 \
  ghcr.io/arabold/docs-mcp-server:latest \
  --protocol http --host 0.0.0.0 --port 6280

</details>

🧠 Configure Embedding Model (Recommended)

Using an embedding model is optional but dramatically improves search quality by enabling semantic vector search.

Example: Enable OpenAI Embeddings

OPENAI_API_KEY="sk-proj-..." npx @arabold/docs-mcp-server@latest

See Embedding Models for configuring Ollama, Gemini, Azure, and others.

📚 Documentation

Getting Started

Installation: Detailed setup guides for Docker, Node.js (npx), and Embedded mode.
Connecting Clients: How to connect Claude, VS Code (Cline/Roo), and other MCP clients.
Basic Usage: Using the Web UI, CLI, and scraping local files.
Configuration: Full reference for config files and environment variables.
Embedding Models: Configure OpenAI, Ollama, Gemini, and other providers.

Key Concepts & Architecture

Deployment Modes: Standalone vs. Distributed (Docker Compose).
Authentication: Securing your server with OAuth2/OIDC.
Telemetry: Privacy-first usage data collection.
Architecture: Deep dive into the system design.

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for development guidelines and setup instructions.

License

This project is licensed under the MIT License. See LICENSE for details.