A Minimum Control Program (MCP) server implementation for web browsing capabilities using BeautifulSoup4
Documentation
✨ 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.
✨ 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 claudeInstalling Manually
Install using uv:
uv tool install web-browser-mcp-serverFor 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
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"
}
})⚙️ 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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