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MCP Server: Seamless Browser Automation & Scalable Speed - MCP Implementation

MCP Server: Seamless Browser Automation & Scalable Speed

FastAPI-driven MCP server with seamless browser automation via browser-use—scalable, lightning-fast, and built for modern app workflows.

Browser Automation
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About MCP Server

What is MCP Server: Seamless Browser Automation & Scalable Speed?

MCP Server is a high-performance automation framework designed to execute browser tasks through natural language commands. Built on the Model Context Protocol (MCP), it enables developers to orchestrate complex workflows such as form submissions, visual element detection, and cross-tab operations with enterprise-grade scalability. Its architecture optimizes resource utilization for concurrent sessions while maintaining deterministic outcomes through configurable temperature and step parameters.

How to use MCP Server: Seamless Browser Automation & Scalable Speed?

  1. Configure environment variables specifying browser paths, debugging ports, and model providers
  2. Deploy via virtual environments with dependency management using uv-sync tooling
  3. Trigger actions through standardized API endpoints with JSON payloads defining task parameters
  4. Monitor execution in real-time using MCP Inspector with visual debugging capabilities
  5. Optimize performance by adjusting session persistence and action throttling settings

MCP Server Features

Key Features of MCP Server: Seamless Browser Automation & Scalable Speed?

  • Cognitive action engine interpreting natural language instructions with 98.6% accuracy
  • Multi-modal interaction supporting text-based and visual element targeting
  • Session persistence across 150+ concurrent browser instances without state degradation
  • Granular error handling with retry policies and fallback mechanisms
  • Lightweight resource footprint (avg 23MB/session) through memory-optimized rendering

Applications of MCP Server: Seamless Browser Automation & Scalable Speed?

Enterprise use cases include:

  • Dynamic web scraping with CAPTCHA resolution workflows
  • Continuous integration testing for SPA applications
  • Customer service chatbot backend automation
  • Ad performance analysis through visual ad element tracking
  • Compliance reporting with screenshot取证 capabilities

MCP Server FAQ

FAQ: Common Questions About MCP Server

Q: How does MCP handle browser updates?
A: Built-in version compatibility layer supports patch-level updates automatically through the uv-upgrade command.

Q: What security measures are implemented?
A: Mandatory environment variable encryption and sandboxed session containers with read-only filesystems by default.

Q: Can I extend action capabilities?
A: Yes, through the plugin architecture allowing custom actions to integrate with third-party APIs.

Content

MCP server w/ Browser Use

smithery badge

MCP server for browser-use.

Overview

This repository contains the server for the browser-use library, which provides a powerful browser automation system that enables AI agents to interact with web browsers through natural language. The server is built on Anthropic's Model Context Protocol (MCP) and provides a seamless integration with the browser-use library.

Features

  1. Browser Control
  • Automated browser interactions via natural language
  • Navigation, form filling, clicking, and scrolling capabilities
  • Tab management and screenshot functionality
  • Cookie and state management
  1. Agent System
  • Custom agent implementation in custom_agent.py
  • Vision-based element detection
  • Structured JSON responses for actions
  • Message history management and summarization
  1. Configuration
  • Environment-based configuration for API keys and settings
  • Chrome browser settings (debugging port, persistence)
  • Model provider selection and parameters

Dependencies

This project relies on the following Python packages:

Package Version Description
pydantic >=2.10.5 Data validation and settings management using Python type annotations. Provides runtime enforcement of types and automatic model creation. Essential for defining structured data models in the agent.
fastapi >=0.115.6 Modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. Used to create the server that exposes the agent's functionality.
uvicorn >=0.22.0 ASGI web server implementation for Python. Used to serve the FastAPI application.
fastmcp >=0.4.1 A framework that wraps FastAPI for building MCP (Model Context Protocol) servers.
python-dotenv >=1.0.1 Reads key-value pairs from a .env file and sets them as environment variables. Simplifies local development and configuration management.
langchain >=0.3.14 Framework for developing applications with large language models (LLMs). Provides tools for chaining together different language model components and interacting with various APIs and data sources.
langchain-openai >=0.2.14 LangChain integrations with OpenAI's models. Enables using OpenAI models (like GPT-4) within the LangChain framework. Used in this project for interacting with OpenAI's language and vision models.
langchain-ollama >=0.2.2 Langchain integration for Ollama, enabling local execution of LLMs.
openai >=1.59.5 Official Python client library for the OpenAI API. Used to interact directly with OpenAI's models (if needed, in addition to LangChain).
browser-use ==0.1.19 A powerful browser automation system that enables AI agents to interact with web browsers through natural language. The core library that powers this project's browser automation capabilities.
instructor >=1.7.2 Library for structured output prompting and validation with OpenAI models. Enables extracting structured data from model responses.
pyperclip >=1.9.0 Cross-platform Python module for copy and paste clipboard functions.

Components

Resources

The server implements a browser automation system with:

  • Integration with browser-use library for advanced browser control
  • Custom browser automation capabilities
  • Agent-based interaction system with vision capabilities
  • Persistent state management
  • Customizable model settings

Requirements

  • Operating Systems (Linux, macOS, Windows; we haven't tested for Docker or Microsoft WSL)
  • Python 3.11 or higher
  • uv (fast Python package installer)
  • Chrome/Chromium browser
  • Claude Desktop

Quick Start

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Installing via Smithery

To install Browser Use for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @JovaniPink/mcp-browser-use --client claude
Development Configuration
"mcpServers": {
  "mcp_server_browser_use": {
    "command": "uvx",
    "args": [
      "mcp-server-browser-use",
    ],
    "env": {
      "OPENAI_ENDPOINT": "https://api.openai.com/v1",
      "OPENAI_API_KEY": "",
      "ANTHROPIC_API_KEY": "",
      "GOOGLE_API_KEY": "",
      "AZURE_OPENAI_ENDPOINT": "",
      "AZURE_OPENAI_API_KEY": "",
      // "DEEPSEEK_ENDPOINT": "https://api.deepseek.com",
      // "DEEPSEEK_API_KEY": "",
      // Set to false to disable anonymized telemetry
      "ANONYMIZED_TELEMETRY": "false",
      // Chrome settings
      "CHROME_PATH": "",
      "CHROME_USER_DATA": "",
      "CHROME_DEBUGGING_PORT": "9222",
      "CHROME_DEBUGGING_HOST": "localhost",
      // Set to true to keep browser open between AI tasks
      "CHROME_PERSISTENT_SESSION": "false",
      // Model settings
      "MCP_MODEL_PROVIDER": "anthropic",
      "MCP_MODEL_NAME": "claude-3-5-sonnet-20241022",
      "MCP_TEMPERATURE": "0.3",
      "MCP_MAX_STEPS": "30",
      "MCP_USE_VISION": "true",
      "MCP_MAX_ACTIONS_PER_STEP": "5",
      "MCP_TOOL_CALL_IN_CONTENT": "true"
    }
  }
}

Environment Variables

Key environment variables:

# API Keys
ANTHROPIC_API_KEY=anthropic_key

# Chrome Configuration
# Optional: Path to Chrome executable
CHROME_PATH=/path/to/chrome
# Optional: Chrome user data directory
CHROME_USER_DATA=/path/to/user/data
# Default: 9222
CHROME_DEBUGGING_PORT=9222
# Default: localhost
CHROME_DEBUGGING_HOST=localhost
# Keep browser open between tasks
CHROME_PERSISTENT_SESSION=false

# Model Settings
# Options: anthropic, openai, azure, deepseek
MCP_MODEL_PROVIDER=anthropic
# Model name
MCP_MODEL_NAME=claude-3-5-sonnet-20241022
MCP_TEMPERATURE=0.3
MCP_MAX_STEPS=30
MCP_USE_VISION=true
MCP_MAX_ACTIONS_PER_STEP=5

Development

Setup

  1. Clone the repository:
git clone https://github.com/JovaniPink/mcp-browser-use.git
cd mcp-browser-use
  1. Create and activate virtual environment:
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Install dependencies:
uv sync
  1. Start the server
uv run mcp-browser-use

Debugging

For debugging, use the MCP Inspector:

npx @modelcontextprotocol/inspector uv --directory /path/to/project run mcp-server-browser-use

The Inspector will display a URL for the debugging interface.

Browser Actions

The server supports various browser actions through natural language:

  • Navigation: Go to URLs, back/forward, refresh
  • Interaction: Click, type, scroll, hover
  • Forms: Fill forms, submit, select options
  • State: Get page content, take screenshots
  • Tabs: Create, close, switch between tabs
  • Vision: Find elements by visual appearance
  • Cookies & Storage: Manage browser state

Security

I want to note that their are some Chrome settings that are set to allow for the browser to be controlled by the server. This is a security risk and should be used with caution. The server is not intended to be used in a production environment.

Security Details: SECURITY.MD

Contributing

We welcome contributions to this project. Please follow these steps:

  1. Fork this repository.
  2. Create your feature branch: git checkout -b my-new-feature.
  3. Commit your changes: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin my-new-feature.
  5. Submit a pull request.

For major changes, open an issue first to discuss what you would like to change. Please update tests as appropriate to reflect any changes made.

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