web-browser-mcp-server

🌐 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.

• 🎯 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

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

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

Install using uv:

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]"

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"
            }
        }
    }
}

The server provides a powerful web browsing tool:

Browse and extract content from web pages with optional CSS selectors:

# 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"
    }
})

Configure through environment variables:

Run the test suite:

python -m pytest

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