What is Browser Mcp

browser-mcp is a Model Control Protocol (MCP) server designed to interface with the browser-use library, enabling AI agents to perform web browsing tasks through a standardized protocol.

Use cases

Use cases for browser-mcp include automating web searches, scraping data from websites, testing web applications, and enabling AI agents to interact with web content dynamically.

How to use

To use browser-mcp, install it via pip or uv, set up the environment with the necessary browser dependencies, and run the MCP server in either development or production mode. You can also use the browser-mcp-run command for automatic dependency checks.

Key features

Key features of browser-mcp include a standardized interface for web browsing tasks, automatic installation of Playwright dependencies, and compatibility with uvx for efficient execution.

Where to use

browser-mcp can be used in various fields such as AI development, web automation, data scraping, and any application requiring automated web browsing capabilities.

browser-mcp

A MCP (Model Control Protocol) server for browser-use library. This package allows AI agents to perform web browsing tasks through a standardized interface.

Installation

You can install the package using pip:

pip install browser-mcp

Or with uv (recommended):

uv pip install browser-mcp

After installation, you’ll need to install Playwright’s browser dependencies:

playwright install

Alternatively, you can use the browser-mcp-run command which will automatically install these dependencies if they’re missing.

Setup

For development, clone the repository and install in development mode:

# Clone the repository
git clone https://github.com/pranav7/browser-mcp.git
cd browser-mcp

# Install dependencies with uv
uv pip install -e .

# Or with pip
pip install -e .

Environment Variables

Create a .env file with your OpenAI API key:

OPENAI_API_KEY=your_api_key_here

Usage

Running the MCP Server

In Development Mode

When working with the package in development mode, you can run it directly with Python:

mcp dev browser_mcp/server.py

In Production

After installing the package from PyPI, you can run it with uvx:

uvx browser-mcp

The package is specifically designed to work with uvx, which allows for more efficient package loading and execution.

With Automatic Dependency Check

You can also use the browser-mcp-run command, which checks for and installs Playwright dependencies automatically before starting the server:

browser-mcp-run

This ensures that all required Playwright browsers are installed on your system.

Using as a Client

from mcp.client import Client

async def main():
    client = await Client.connect()

    # Perform a task with the browser
    result = await client.rpc("perform_task_with_browser",
                             task="Search for the latest news about AI and summarize the top 3 results")
    print(result)

    await client.close()

Programmatic Usage

You can also use the package programmatically:

# In development mode
from src import run

# In production (after installing the package)
# from browser_mcp import run

# Run the MCP server with stdio transport
run(transport="stdio")

# Or with SSE transport
# run(transport="sse")

Available RPC Methods

• search_web(task: str, model: str = "gpt-4o-mini") - Performs basic web searches using browser-use Agent. The model parameter is optional and defaults to “gpt-4o-mini”.
• search_web_with_planning(task: str, base_model: str = "gpt-4o-mini", planning_model: str = "o3-mini") - Performs complex web searches that require planning. Uses a planner LLM for better task decomposition. Both base_model and planning_model parameters are optional with their respective defaults.

Development

Testing

Tests can be run with:

python -m unittest discover

You can also test the package functionality with:

python test_uvx.py

This script will:

1. Test importing the package directly (development mode)
2. Attempt to run it with uvx (production mode)

Note: The uvx test may fail in development mode unless the package is published to PyPI. This is expected behavior.

Publishing to PyPI

This project uses GitHub Actions to automatically publish to PyPI when a new release is created. The workflow:

1. Builds the package using uv
2. Publishes it to PyPI using trusted publishing

To create a new release:

1. Update the version in pyproject.toml
2. Create a new release on GitHub
3. The GitHub Action will automatically build and publish the package

License

MIT License