tabby-mcp

🚀 Tabby-MCP-Server

Powerful Tabby plugin that implements Model Context Protocol (MCP) server, enabling AI-powered terminal control and automation.

📹 Video Demo

Watch the full video demonstration of Tabby-MCP in action:

✨ Features

• 🤖 AI Connection: Seamlessly connect AI assistants to your terminal
• 🔌 MCP Server: Built-in Model Context Protocol server implementation
• 🖥️ Terminal Control: Allow AI to execute commands and read terminal output
• 🔍 Session Management: View and manage SSH sessions
• 🚫 Command Abort: Safely abort running commands
• 📋 Buffer Access: Retrieve terminal buffer content with flexible options

📋 Table of Contents

• Installation
• Quick Start
• Usage Examples
• Configuration
• API Reference
• Contributing
  • Continuous Integration
• License

🔧 Installation

Using Docker

You can build and install the plugin using Docker with the following command:

git clone https://github.com/thuanpham582002/tabby-mcp-server.git
cd tabby-mcp-server
# Build the Docker image
docker build -t tabby-mcp . && docker run -v $(pwd)/build:/output tabby-mcp
bash scripts/copy_to_plugin_folder.sh

This command builds a Docker image tagged as 'tabby-mcp' and runs a container from this image, mounting your local 'build' directory to '/output' in the container. The script scripts/copy_to_plugin_folder.sh will copy the built files to the Tabby plugin folder.

Note: Our CI/CD workflows on GitHub also use this Docker-based build process to ensure consistency between local development and production releases.

🚀 Quick Start

1. Install the plugin
2. Configure your Tabby environment
3. Connect to MCP server from any of the supported clients listed at https://modelcontextprotocol.io/clients

💻 Usage Examples

Connect an AI to Control Your Terminal

Retrieve SSH Session List

⚙️ Configuration

Configure the MCP server through the Tabby settings:

{
  "mcp": {
    "port": 3001,
    "host": "http://localhost:3001", // note: in development
    "enableLogging": false,
    "startOnBoot": true
  }
}

MCP Client Configuration

When connecting to the Tabby MCP server from an AI client (like Claude, GPT, etc.), use the following configuration:

{
  "mcpServers": {
    "Tabby MCP": {
      "url": "http://localhost:3001/sse"
    }
  }
}

You may need to adjust the url parameter if you've configured a different host or port in your server settings.

📚 API Reference

Available Tools

Tool	Description	Parameters
getSshSessionList	Get list of SSH sessions	None
execCommand	Execute a command in terminal	command, tabId
getTerminalBuffer	Get terminal content	tabId, startLine, endLine
abortCommand	Abort a running command	None

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

See the contributing guidelines for more details.

Continuous Integration

Our project uses GitHub Actions for CI/CD with Docker-based builds:

• Pull Requests: Automatically built and tested using Docker to ensure compatibility
• Main Branch: Builds with Docker, publishes to npm, and creates GitHub releases
• Benefits: Consistent environment across development, testing, and production

To set up the CI/CD pipeline in your fork:

1. Configure the required secrets in your repository settings:

   • NPM_TOKEN: Your npm access token for publishing
   • GITHUB_TOKEN: Automatically provided by GitHub Actions

2. The workflows will automatically run on push and pull request events.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by Pham Tien Thuan