browser-mcp 0.1.1

MCP server for browser-use

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

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:

python -m src

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.

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

• perform_task_with_browser(task: str) - Performs a specified task using the browser-use Agent with GPT-4o-mini

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