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Testkube MCP Server

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Check out https://testkube.io/blog/building-ai-assisted-test-workflows-testkube-mcp-github-copilot for a complete example on AI-driven remediation using the Testkube MCP Server with Github and Vs-Code.

Overview​

The Testkube MCP Server brings Testkube's test orchestration capabilities directly into your development environment and AI-powered workflows, enabling AI assistants and agents to interact directly with your Testkube workflows, executions, and artifacts.

More specifically, it allows you and your AI agents to:

  • Execute and Monitor Test Workflows: Run workflows, check execution status, and retrieve results
  • Analyze Test Results: Access execution logs, artifacts, and failure details
  • Navigate Test History: Search through past executions and analyze trends
  • Create and Manage Test Resources: List workflows, view configurations, create workflows and access metadata
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Model Context Protocol (MCP) is an open standard that enables AI assistants to connect with external systems and data sources.

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When used with agentic AI tools like GitHub Copilot with Claude Sonnet 4 in VS Code or Cursor, the Testkube MCP enables:

  • Multi-step Problem Solving: AI agents can run multiple tools in sequence to solve complex testing scenarios.
  • Automated Debugging: Agents can analyze failures, examine logs, and suggest fixes.
  • Intelligent Test Management: Automated workflow creation, execution, and result analysis.

Getting Started​

Choose the setup method that works best for you:

The easiest way to get started - Connect directly to the Testkube Control Plane without any local installation.

  • ✅ No installation required
  • ✅ Always available
  • ✅ Best for remote access and team collaboration
  • ✅ Works with any AI tool that supports SSE transport

Get Started with Hosted Endpoint →

2. Testkube CLI​

Run the MCP Server locally using the Testkube CLI for full control and local development.

  • 🔧 Full control over the MCP server process
  • 🔧 Best for local development
  • 🔧 Supports stdio and shttp transports
  • 🔧 Works with all MCP-compatible tools

Get Started with CLI →

3. Docker Container​

Run the MCP Server in a Docker container for containerized deployments.

  • 🐳 Container-friendly deployment
  • 🐳 Ideal for CI/CD pipelines
  • 🐳 Easy to integrate with existing Docker workflows
  • 🐳 Supports both stdio and shttp transports

Get Started with Docker →

Configuration Examples​

Once you've set up the Testkube MCP Server, configure your AI tools:

Available Tools​

The MCP server provides 20 tools for comprehensive Testkube management:

Workflow Management​

  • list_workflows - List workflows with filtering
  • get_workflow - Get workflow details
  • get_workflow_definition - Get workflow YAML
  • get_workflow_metrics - Get workflow performance metrics
  • create_workflow - Create new workflow
  • update_workflow - Update existing workflow
  • run_workflow - Execute workflow

Agent Management​

  • list_agents - List available agents for workflow execution targeting - Read More

Execution Management​

  • list_executions - List workflow executions
  • get_execution_info - Get execution details
  • get_workflow_execution_metrics - Get detailed resource consumption metrics for a specific execution
  • fetch_execution_logs - Get execution logs
  • lookup_execution_id - Resolve execution name to ID
  • wait_for_executions - Wait for executions to complete
  • abort_workflow_execution - Cancel running execution

Artifact Management​

  • list_artifacts - List execution artifacts
  • read_artifact - Read artifact content

Utility Tools​

  • build_dashboard_url - Generate dashboard URLs
  • list_labels - List available labels
  • list_resource_groups - List resource groups

Example Prompts​

Once configured, you can interact with Testkube using natural language:

List my test workflows and their recent execution status
Help me debug my last failed workflow execution
Create a new test workflow for my Python API and run it 
Show me test execution trends for the past week and identify failing patterns
Analyze the logs of execution "api-tests-123" and suggest fixes

Need Help?​

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Don't hesitate to reach out to us on Slack if you run into any issues!