Terminal Control MCP

Terminal Control MCP

Enables AI agents to interact with terminal-based TUI applications by capturing visual terminal output as PNG screenshots and simulating keyboard input through a virtual X11 display.

Category
Visit Server

README

Terminal Control MCP

A Model Context Protocol (MCP) server that enables AI agents to interact with terminal-based TUI applications through a virtual X11 display approach.

Overview

This project provides a comprehensive solution for controlling terminal applications programmatically using:

  • Xvfb for headless virtual X11 display
  • xterm for terminal emulation
  • xdotool for input simulation and window management
  • ImageMagick for PNG screenshot capture

The system captures actual visual terminal output as PNG screenshots, making it ideal for AI agents that need to see and interact with terminal applications.

Features

  • Virtual Display Management: Headless X11 display using Xvfb
  • Input Simulation: Send keyboard input and text to terminal applications
  • Screenshot Capture: Take PNG screenshots of terminal output
  • Window Management: Reliable window detection and focus handling
  • Resource Cleanup: Proper process management with timeout handling

System Requirements

The following system packages must be installed:

# Ubuntu/Debian
sudo apt-get install xvfb xterm xdotool imagemagick

# CentOS/RHEL/Fedora
sudo yum install xorg-x11-server-Xvfb xterm xdotool ImageMagick

Installation

This project uses the uv package manager:

# Clone the repository
git clone <repository-url>
cd terminal-control-mcp

# Install dependencies
uv sync

# Activate virtual environment
source .venv/bin/activate

Quick Start

Running the Example

Try the included htop example to see the system in action:

python examples/example_htop.py

This will:

  1. Launch htop in a virtual xterm session
  2. Press F3 to open the search dialog
  3. Type "python" as a search term
  4. Capture PNG screenshots at each step
  5. Clean up all processes

Basic Usage

from examples.example_htop import XTermSession

# Create a session
session = XTermSession(width=1920, height=1080)

try:
    # Start virtual display and terminal
    session.start_virtual_display()
    session.start_xterm("your-command-here")
    
    # Take a screenshot
    session.take_screenshot("output.png")
    
    # Send input
    session.send_key("F1")
    session.send_text("hello world")
    
finally:
    session.cleanup()

Architecture

Core Components

  1. XTermSession Class: The main interface for terminal control

    • Manages Xvfb virtual display lifecycle
    • Spawns and controls xterm processes
    • Handles input simulation via xdotool
    • Captures screenshots using ImageMagick
  2. Virtual Display Approach: Unlike direct TTY manipulation, this system:

    • Creates a real X11 environment with Xvfb
    • Launches actual xterm instances
    • Captures genuine visual output as PNG files
    • Provides reliable input simulation

Key Methods

  • start_virtual_display(): Initialize Xvfb virtual display
  • start_xterm(command): Launch xterm with specified command
  • send_key(key): Send special keys (F1, Escape, etc.)
  • send_text(text): Send alphanumeric text input
  • take_screenshot(filename): Capture PNG screenshot
  • cleanup(): Properly terminate all processes

Development

Project Structure

terminal-control-mcp/
├── src/terminal_control_mcp/    # Main MCP server implementation (planned)
├── examples/
│   ├── example_htop.py         # Reference implementation
│   └── README.md
├── tests/                      # Test suite
├── pyproject.toml             # Project configuration
└── CLAUDE.md                  # Development guidelines

Development Commands

# Run the main application
python main.py

# Run the htop example
python examples/example_htop.py

# Activate virtual environment
source .venv/bin/activate

MCP Server Implementation (Planned)

The full MCP server will provide these tools:

  • terminal_launch: Start a new terminal session
  • terminal_input: Send keyboard/text input
  • terminal_capture: Take PNG screenshot
  • terminal_close: Clean up terminal session

Technical Details

Window Detection

The system uses multiple fallback strategies for reliable window ID detection:

search_methods = [
    ['xdotool', 'search', '--class', 'XTerm'],
    ['xdotool', 'search', '--name', 'xterm'],
    ['xdotool', 'search', '--class', 'xterm'],
    ['xdotool', 'getactivewindow']
]

Screenshot Capture

Uses ImageMagick's import command for reliable PNG capture:

subprocess.run(['import', '-window', 'root', filename], env=env)

Resource Management

Implements proper cleanup with timeout handling:

def cleanup(self):
    if self.xterm_proc:
        self.xterm_proc.terminate()
        try:
            self.xterm_proc.wait(timeout=5)
        except subprocess.TimeoutExpired:
            self.xterm_proc.kill()

Contributing

[TBD]

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured