Enable AI assistants to intelligently select and execute your automation workflows
Qontinui Runner can be controlled by AI assistants like Claude through the MCP (Model Context Protocol) server. For AI to make intelligent decisions about which workflows to run and in what order, your workflows need rich, structured descriptions.
This guide explains how to write workflow descriptions that AI can understand, enabling autonomous development workflows where AI verifies code changes by running your automation.
When you ask an AI assistant to “verify the login feature works,” it needs to understand:
Good descriptions enable AI to autonomously run the right workflows, analyze results, and even fix issues it discovers.
Use the existing description field in your workflow. No additional fields are needed. Structure your description with these sections:
[One-line summary of what this workflow does] Use when: [Conditions that indicate this workflow should be run] Verifies: [What features/functionality this workflow tests] Prerequisites: [What must be true before running] Produces: [What state changes or outputs result from running] Depends on: [Other workflows that must run first, if any] Success indicators: [How to know the workflow succeeded] Failure indicators: [Signs that something went wrong]
| Field | Required | Description |
|---|---|---|
| Summary | Yes | First line, brief description of the workflow's action |
| Use when | Yes | Conditions/situations when this workflow is appropriate |
| Verifies | Recommended | Features or functionality being tested |
| Prerequisites | Recommended | Required state before running (apps open, logged in, etc.) |
| Produces | Optional | Side effects or outputs (new data, state changes) |
| Depends on | Optional | Other workflow names that must run first |
| Success indicators | Optional | How to verify success (visible elements, data created) |
| Failure indicators | Optional | Signs of failure (error messages, missing elements) |
Clicks Build > State Machine in the website navigation menu to open the State Machine Builder page. Use when: Need to test or verify the State Machine Builder feature, or after making changes to state machine related code. Verifies: Navigation menu works, State Machine Builder page loads, canvas renders correctly. Prerequisites: Website running on localhost:3001, user logged in to the application. Success indicators: State Machine canvas is visible, no console errors, page title shows "State Machine". Failure indicators: 404 error, blank page, console errors, navigation menu not responding.
Opens the runner's extraction panel and performs a new web extraction on the currently visible application. Use when: Need to create new extraction data for testing, or to verify the extraction feature works after code changes. Verifies: Runner extraction panel opens, screenshot capture works, element detection runs, states are identified. Prerequisites: Qontinui Runner is running, target application is visible on screen, a project is loaded. Produces: New extraction data (states, images, elements) in the current project configuration. Success indicators: Extraction completes without errors, at least one state is detected, images are captured. Failure indicators: Extraction hangs, no states detected, "0 items found" in logs, screenshot capture fails.
Navigates to the Web Extraction page in the website and verifies that extraction data is displayed correctly. Use when: After creating new extraction data, need to verify it appears correctly in the web interface. Verifies: Web Extraction page loads, extraction data is displayed, images render correctly, state list is populated. Prerequisites: Website running, user logged in, extraction data exists in the project. Depends on: "Start New Web Extraction" (if no extraction data exists yet) Success indicators: Extraction data visible in the UI, images load, state count matches expected. Failure indicators: Empty state list, broken images, "No extractions found" message, API errors.
For complex verification tasks, AI assistants can chain multiple workflows together. The Depends on field helps AI understand the correct order.
When you ask AI to “verify web extraction works end-to-end,” it will:
When you ask an AI assistant like Claude to run automation, it follows this process:
Reads the workflow configuration file to see all available workflows
Extracts "Use when" and "Verifies" fields to understand each workflow's purpose
Compares your request against workflow purposes to find relevant matches
Reads "Depends on" fields to determine correct execution order
Runs workflows in sequence via the MCP server
Checks success/failure indicators in logs and screenshots
Reports findings and can autonomously fix issues discovered during verification
AI assistants interact with Qontinui Runner through the MCP (Model Context Protocol) server. The key commands are:
# Load a workflow configuration
mcp__qontinui__load_config("/path/to/config.json")
# Check what workflows are available
mcp__qontinui__get_loaded_config()
# Run a specific workflow
mcp__qontinui__run_workflow("Navigate to State Machine Builder")
# Run on a specific monitor
mcp__qontinui__run_workflow("My Workflow", monitor="left")The MCP server is available as qontinui-mcp on PyPI and works with Claude Desktop, Claude Code, Cursor, and other MCP-compatible tools.
Ready to enable AI-powered automation? Here's how to get started:
pip install qontinui-mcp