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✨ Features

> 🌐 Enable AI assistants to browse and extract content from the web through a simple MCP interface.

The Web Browser MCP Server provides AI models with the ability to browse websites, extract content, and understand web pages through the Message Control Protocol (MCP). It enables smart content extraction with CSS selectors and robust error handling.

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🤝 Contribute •
📝 Report Bug

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✨ Core Features

- 🎯 Smart Content Extraction: Target exactly what you need with CSS selectors
- ⚡ Lightning Fast: Built with async processing for optimal performance
- 📊 Rich Metadata: Capture titles, links, and structured content
- 🛡️ Robust & Reliable: Built-in error handling and timeout management
- 🌍 Cross-Platform: Works everywhere Python runs

🚀 Quick Start

Installing via Smithery

To install Web Browser Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install web-browser-mcp-server --client claude

Installing Manually

uv tool install web-browser-mcp-server

For development:

# Clone and set up development environment
git clone https://github.com/blazickjp/web-browser-mcp-server.git
cd web-browser-mcp-server
Create and activate virtual environment
uv venv
source .venv/bin/activate
Install with test dependencies
uv pip install -e ".[test]"

🔌 MCP Integration

Add this configuration to your MCP client config file:

{
    "mcpServers": {
        "web-browser-mcp-server": {
            "command": "uv",
            "args": [
                "tool",
                "run",
                "web-browser-mcp-server"
            ],
            "env": {
                "REQUEST_TIMEOUT": "30"
            }
        }
    }
}

For Development:

{
    "mcpServers": {
        "web-browser-mcp-server": {
            "command": "uv",
            "args": [
                "--directory",
                "path/to/cloned/web-browser-mcp-server",
                "run",
                "web-browser-mcp-server"
            ],
            "env": {
                "REQUEST_TIMEOUT": "30"
            }
        }
    }
}

💡 Available Tools

The server provides a powerful web browsing tool:

browse_webpage

# Basic webpage fetch
result = await call_tool("browse_webpage", {
    "url": "https://example.com"
})
Target specific content with CSS selectors
result = await call_tool("browse_webpage", {
    "url": "https://example.com",
    "selectors": {
        "headlines": "h1, h2",
        "main_content": "article.content",
        "navigation": "nav a"
    }
})

⚙️ Configuration

Configure through environment variables:

| Variable | Purpose | Default |
|----------|---------|---------|
| REQUEST_TIMEOUT | Webpage request timeout in seconds | 30 |

🧪 Testing

Run the test suite:

python -m pytest

📄 License

Released under the MIT License. See the LICENSE file for details.

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Made with ❤️ by the Pear Labs Team

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