MCP Servers

Qontinui MCP Server

Enables AI-driven visual automation by connecting to Qontinui Runner to load workflow configurations, execute visual automation workflows, and monitor execution status across multiple displays.

README

qontinui-mcp

Lightweight MCP server for Qontinui Runner - enables AI-driven visual automation.

Installation

pip install qontinui-mcp

Quick Start

Start the Qontinui Runner (desktop application)
Configure your AI client (Claude Desktop, Claude Code, Cursor, etc.)

Add to your MCP configuration:

{
  "mcpServers": {
    "qontinui": {
      "command": "qontinui-mcp",
      "args": []
    }
  }
}

Run workflows via AI

The AI can now:

Load workflow configuration files
Run visual automation workflows
Monitor execution status
Control which monitor to use

Configuration

Environment variables:

Variable	Description	Default
`QONTINUI_RUNNER_HOST`	Runner host address	Auto-detected (WSL-aware)
`QONTINUI_RUNNER_PORT`	Runner HTTP port	`9876`

Available Tools

Tool	Description
`get_executor_status`	Get runner status
`list_monitors`	List available monitors
`load_config`	Load a workflow configuration file
`ensure_config_loaded`	Load config if not already loaded
`get_loaded_config`	Get loaded configuration info
`run_workflow`	Run a workflow by name
`stop_execution`	Stop current execution

Example Usage

# In an AI conversation:
"Load the config at /path/to/workflow.json and run the 'login_test' workflow on the left monitor"

Development

# Clone
git clone https://github.com/qontinui/qontinui-mcp
cd qontinui-mcp

# Install dependencies
poetry install

# Run server locally
poetry run qontinui-mcp

License

MIT

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