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Docs MCP Server solves the problem of AI hallucinations and outdated knowledge by providing a personal, always-current documentation index for your AI coding assistant.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, Office documents (Word, Excel, PowerPoint), OpenDocument, RTF, EPUB, Jupyter Notebooks, and 90+ source code languages.

---

📄 Supported Formats

See **Supported Formats** for the complete reference including MIME types and processing details.

---

🚀 Quick Start

CLI First

For agents and scripts, the CLI is usually the simplest way to use Grounded Docs.

1. Index documentation (requires Node.js 22+):

npx @arabold/docs-mcp-server@latest scrape react https://react.dev/reference/react

2. Query the index:

npx @arabold/docs-mcp-server@latest search react "useEffect cleanup" --output yaml

3. Fetch a single page as Markdown:

npx @arabold/docs-mcp-server@latest fetch-url https://react.dev/reference/react/useEffect

Output Behavior

• Structured commands default to clean JSON on stdout in non-interactive runs.
• Use --output json|yaml|toon to pick a structured format.
• Plain-text commands such as fetch-url keep their text payload on stdout.
• Diagnostics go through the shared logger and are kept off stdout in non-interactive runs.
• Use --quiet to suppress non-error diagnostics or --verbose to enable debug output.

Agent Skills

The [skills/](skills/) directory contains Agent Skills that teach AI coding assistants how to use the CLI — covering documentation search, index management, and URL fetching.

MCP Server

If you want a long-running MCP endpoint for Claude, Cline, Copilot, Gemini CLI, or other MCP clients:

1. Start the server:

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.

Alternative: Run with Docker

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

🧠 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.
• **Supported Formats**: Complete file format and MIME type reference.
• **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.