MCP Browser Agent
# MCP Browser Agent
[](https://smithery.ai/server/@ashley-ha/mcp-manus)
Built at AGI House MCP Hackathon
<a href="https://glama.ai/mcp/servers/@ashley-ha/mcp-manus">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@ashley-ha/mcp-manus/badge" alt="Browser Agent MCP server" />
</a>
## Overview
This project is a browser automation agent that uses the Model Context Protocol (MCP) to enable browser interactions. It provides a seamless integration between Claude and browser automation capabilities through our MCP server.
Thank you to Browser-Use for their browser agent capabilities that help power our MCP server!
## System Requirements
- macOS (darwin 24.2.0)
- Python 3.12 or higher
- `uv` package manager
- Google Chrome browser (Ensure your browser is closed before running task(s).)
## Installation
### Installing via Smithery
To install Browser Automation Agent for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@ashley-ha/mcp-manus):
```bash
npx -y @smithery/cli install @ashley-ha/mcp-manus --client claude
```
### Manual Installation
1. Clone the repository:
```bash
git clone <repository-url>
cd mcp
```
2. Set up the Python environment using `uv`:
```bash
uv venv
source .venv/bin/activate
uv sync
```
## Configuration
### Claude Desktop Configuration
Create or modify your Claude Desktop configuration file:
```json
{
"mcpServers": {
"browser-use": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/mcp",
"run",
"browser-use.py"
]
}
}
}
```
Replace `/ABSOLUTE/PATH/TO/browser-use` with the absolute path to your project directory.
### Browser Configuration
The agent is configured to use Google Chrome with the following default settings:
- Non-headless mode for development
- Window size: 1280x1100
- Disabled security features for testing
- Recording path: ./tmp/recordings
## Features
- Browser automation through MCP tools
- State management and planning capabilities
- Interactive element detection and manipulation
- Configurable browser contexts
- Logging and debugging support
## Usage
The agent provides two main tools:
1. `get_planner_state`: Retrieves the current browser state and planning context
2. `execute_actions`: Executes planned actions in the browser
## Development
### Logging
The project uses Python's built-in logging with the following configuration:
- All logs are directed to stderr
- Custom formatting: `%(levelname)-8s [%(name)s] %(message)s`
- Root logger level: INFO
- Third-party loggers level: WARNING
### Project Structure
- `browser-use.py`: Main entry point and server implementation
- `tmp/recordings`: Directory for browser session recordings
- Dependencies managed through `uv`
## Contributing
This project was built during the AGI House MCP Hackathon. Contributions are welcome!
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Copyright (c) 2025 Jaeyun Ha, Ashley Ha
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.TDQS
Scored across 2 tools
The two tools have completely distinct purposes: get_planner_state retrieves browser state and planning context, while execute_actions performs actions based on that state. There is no overlap or ambiguity between these functions.
Both tools follow a consistent verb_noun pattern with clear action-oriented names (get_planner_state, execute_actions). The naming convention is uniform and predictable throughout the set.
With only 2 tools for a browser automation server, the surface feels severely limited. While the tools cover a basic planning-execution loop, typical browser automation requires more granular operations like navigation, element interaction, or content extraction.
The toolset provides only a high-level planning/execution abstraction without direct browser manipulation capabilities. There are significant gaps for common browser tasks like navigating to URLs, clicking elements, extracting text, or handling dialogs, which agents would need for robust automation.