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MCP Browser Server

by Wladastic

MCP Browser Server

A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. This server enables AI assistants to interact with web pages through a standardized interface.

Perfect for web automation, testing, and debugging workflows with AI assistants including:

  • Chat.fans agents - Empower AI agents with web interaction capabilities in VS Code
  • GitHub Copilot Chat - Enhance your development workflow with browser automation
  • Any MCP-compatible AI assistant - Universal browser automation for AI tools

Features

  • Multi-browser support: Chromium, Firefox, and WebKit
  • Comprehensive automation: Navigate, click, type, screenshot, and more
  • JavaScript execution: Run custom scripts in the browser context
  • Element interaction: Wait for elements, get text content, and interact with forms
  • Screenshot capabilities: Capture full pages or viewport screenshots
  • Type-safe: Built with TypeScript and runtime validation using Zod image

Installation

npm install npm run build

Make sure Playwright browsers are installed:

npx playwright install

For system dependencies (Linux):

sudo npx playwright install-deps

Usage

VS Code Integration

Configure the MCP server in VS Code by adding to your settings.json or workspace configuration:

"mcp": { "servers": { "browser-automation": { "command": "node", "args": [ "/home/yourUserName/mcp-browser-server/build/index.js" ], "env": {} } } }

Once configured, Chat.fans agents and GitHub Copilot Chat can use browser automation tools for web testing, scraping, and automation tasks.

Available VS Code Tasks
  • Build: Ctrl+Shift+P → "Tasks: Run Task" → "build"
  • Development Mode: Ctrl+Shift+P → "Tasks: Run Task" → "dev"
  • Test MCP Server: Ctrl+Shift+P → "Tasks: Run Task" → "test-mcp-server"

Available Tools

  1. launch_browser - Start a new browser instance
  2. navigate - Go to a specific URL
  3. click_element - Click on page elements
  4. type_text - Enter text into form fields
  5. screenshot - Capture page screenshots
  6. get_element_text - Extract text from elements
  7. wait_for_element - Wait for elements to appear/disappear
  8. evaluate_javascript - Run custom JavaScript
  9. get_console_logs - Get browser console logs (log, info, warn, error, debug)
  10. analyze_screenshot - AI-powered screenshot analysis using Gemma3 (requires Ollama)
  11. get_page_info - Get current page information
  12. close_browser - Close the browser instance

Example: Web Application Testing

// Launch browser in headed mode for visual debugging await launch_browser({ browser: "chromium", headless: false }); // Navigate to login page await navigate({ url: "http://localhost:3000/login" }); // Fill in credentials await type_text({ selector: "input[type='email']", text: "user@example.com" }); await type_text({ selector: "input[type='password']", text: "password123" }); // Submit form await click_element({ selector: "button[type='submit']" }); // Wait for successful login await wait_for_element({ selector: ".dashboard", timeout: 10000 }); // Check for any console errors during login await get_console_logs({ level: "error" }); // Take screenshot of dashboard await screenshot({ fullPage: true, path: "dashboard.png" }); // Get all console logs for debugging await get_console_logs();

AI-Powered Screenshot Analysis

The analyze_screenshot tool provides AI-powered analysis of web pages using local Gemma3 models via Ollama. This feature can describe what's visible on a page, analyze page structure, and look for specific elements based on context.

Prerequisites

  1. Install Ollama: Download from ollama.ai
  2. Install Gemma3 model:
    ollama pull gemma3:4b
  3. Start Ollama service:
    ollama serve

Usage Examples

Basic Screenshot Analysis
// Take and analyze a screenshot with AI await analyze_screenshot({ fullPage: true, model: "gemma3:4b" });
Detailed Structural Analysis
// Get detailed analysis of page structure await analyze_screenshot({ detailed: true, pretext: "Focus on navigation elements and form fields" });
Context-Specific Analysis
// Look for specific elements or issues await analyze_screenshot({ pretext: "Check if there are any error messages or broken layouts", path: "error-check.png" });

Parameters

  • fullPage (boolean): Capture entire scrollable page vs viewport only
  • path (string): Optional file path to save the screenshot
  • pretext (string): Additional context or specific instructions for the AI
  • model (string): AI model to use (default: "gemma3:4b")
  • detailed (boolean): Request detailed structural analysis

Supported Models

  • gemma3:4b (default, good balance of speed and quality)
  • Any other vision-capable model available in your Ollama installation

Development & Testing

Quick Setup

# One-command setup (installs dependencies, browsers, and builds) npm run setup # Or step by step: npm install npx playwright install npm run build

Development Commands

# Build the project npm run build # Run in development mode npm run dev # Start the server npm run start # Development helper (shows all available commands) npm run dev-helper help

Testing

The project includes comprehensive tests in the tests/ directory:

# Run basic communication test npm run test # Run browser automation demo npm run test:demo # Run AI analysis test (requires Ollama) npm run test:ai-simple # Check system status npm run test:status # Run all tests npm run test:all

Development Helper

Use the development helper for common tasks:

# Show all available commands npm run dev-helper help # Quick setup from scratch npm run dev-helper setup # Run comprehensive tests npm run dev-helper test # Clean generated files npm run dev-helper clean

For more details about testing, see tests/README.md.

Project Structure

mcp-browser-server/ ├── src/ # TypeScript source code │ └── index.ts # Main MCP server implementation ├── build/ # Compiled JavaScript output ├── tests/ # Test scripts and documentation │ ├── README.md # Testing documentation │ ├── simple-test.mjs # Basic communication test │ ├── demo-test.mjs # Browser automation demo │ └── *.mjs # Additional test files ├── screenshots/ # Generated screenshots from tests ├── package.json # Project configuration └── README.md # This file

License

Dual License:

  • Personal Use: Free for personal, educational, and non-commercial use
  • Commercial Use: Requires a separate commercial license

See LICENSE for full terms. For commercial licensing inquiries, please contact us.

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security - not tested
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license - not found
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quality - not tested

A Model Context Protocol server that enables AI assistants to interact with web browsers through Playwright, providing automation capabilities for navigation, interaction, and screenshots.

  1. Features
    1. Installation
      1. Usage
        1. VS Code Integration
        2. Available Tools
      2. Example: Web Application Testing
        1. AI-Powered Screenshot Analysis
          1. Prerequisites
          2. Usage Examples
          3. Parameters
          4. Supported Models
        2. Development & Testing
          1. Quick Setup
          2. Development Commands
          3. Testing
          4. Development Helper
          5. Project Structure
        3. License

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