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Testiny MCP Server

The Testiny MCP (Model Context Protocol) Server enables AI tools like Claude (Code, Desktop), VS Code with Copilot, and other MCP-compatible applications to interact directly with your Testiny project data. This allows AI assistants to create and read test cases, create test runs, update test results, and manage your testing workflow seamlessly.

Why use Testiny with AI assistants?​

The Testiny MCP Server lets AI assistants work directly with your test management data. Instead of manually searching for test cases, creating runs, or updating results, you can describe what you want in natural language and let your AI assistant perform the work.

Connect Testiny to Claude, ChatGPT, GitHub Copilot, Cursor, or any MCP-compatible client to:

  • Create and organize test cases
  • Search and analyze existing test repositories
  • Generate new tests from requirements or user stories
  • Create and manage test runs
  • Execute tests and update results
  • Track testing progress across projects

Setup​

To use the Testiny MCP server, you'll need to configure it with your Testiny credentials and add it to your AI tool's MCP configuration.

Prerequisites​

  • A Testiny account with API access
  • A Testiny paid or trial plan
  • An optional API key from your Testiny API Keys settings
  • If no API key is used, the Authorization header is not needed and OAuth will be used instead

Configuration Examples​

Here are examples of how to configure the MCP server with specific clients. You can use the MCP server with any MCP compatible client (protocol '2025-06-18').

Configure the Testiny MCP server in your VS Code settings mcp.json or workspace configuration:

{
  "servers": {
    "testiny": {
      "url": "https://app.testiny.io/api/v1/mcp-server",
      "type": "http",
      "headers": {
        "Authorization": "Bearer your-api-key-here"
      }
    }
  }
}
Security Notice

Treat your API key like a password and keep it secure. The API key inherits the same permissions as the user who created it. Consider creating dedicated API keys with limited project access for MCP server usage.

Available Tools​

The Testiny MCP server provides the following tools for AI assistants to interact with your Testiny data:

Project Management​

  • listProjects - Retrieve all accessible Testiny projects
  • listTestCaseFolders - Browse test case folder structure within projects
  • listTestCases - Get test cases with optional keyword filtering
  • listTestRuns - Get test runs with optional keyword filtering

Test Cases & Folders​

  • createFolder - Create a folder with a title, and optionally a description and parent folder
  • editFolder - Edit the title or description of a folder
  • moveFolder - Move a folder into other folders
  • createTestCase - Create test cases (in a folder) with a precondition, steps, expected results and custom fields
  • editTestCase - Edit the title, precondition, steps and custom fields
  • moveTestCases - Move test cases into (other) folders

Test Execution​

  • createTestRun - Create a test run for organizing test execution
  • setTestResult - Update a test case result (passed, failed, blocked, skipped) in a test run
  • setTestResultBatch - Update test case results (passed, failed, blocked, skipped) in a test run
  • getTestStatus - Check current test case status for one or all test cases within a test run
  • addTestResultComment - Add a comment to a test result

Troubleshooting​

If you encounter issues with the MCP server:

  1. Verify API Key: When using an API key, ensure your API key is valid and has appropriate permissions
  2. Ensure OAuth enabled: Ensure that OAuth is enabled in your Testiny organization settings
  3. MCP feature available: Make sure you have a paid Testiny plan or are in the trial phase
  4. Check Base URL: Confirm the base URL matches your Testiny instance (Cloud vs Server)
  5. Project Access: Verify the API key's user has access to the projects you're trying to access

For additional support or feature requests, please contact our support team as explained here.

Use Cases​

Test Cases as Context​

Your test repository contains valuable knowledge about how your application works. Test cases often capture business rules, edge cases, validation requirements, and expected behavior that may not exist in specifications or documentation.

With the Testiny MCP Server, AI assistants can use this information as context when helping with development and testing tasks.

For example:

"Generate API tests for the new subscription feature based on our existing billing test cases."

An AI assistant can:

  1. Search related billing and subscription test cases
  2. Identify existing patterns and business rules
  3. Generate consistent new test cases
  4. Create them directly in Testiny

Faster Test Planning​

When new requirements arrive, AI assistants can help identify what needs testing. You can also connect with other MCP servers (for example, Jira MCP or GitLab MCP) to leverage project information, requirements, issues and merge requests.

Example prompt:

"Review these requirements and suggest missing test cases based on our current regression suite."

The assistant can compare requirements against existing test coverage and propose additional test cases or exploratory testing ideas.

Streamlined Test Execution​

AI assistants can also help manage execution activities.

Example prompt:

"Create a regression test run for Sprint 24 and mark all authentication tests as passed."

The assistant can locate the appropriate project, create the test run, and update results through the MCP tools.

Example Workflows​

Generate Test Cases from Requirements​

When new functionality is introduced, AI assistants can transform requirements into structured test cases and create the test cases directly in Testiny.

"Create test cases for password reset functionality in the Web Application project."

"Review the requirements TNY-123, TNY-124 and TNY-125 and create test cases in project Web Application."

Prompt Hint

Use specific prompts, e.g. define the Testiny project you're working in.

Analyze Test Coverage & Investigate Failed Tests​

Understanding what is tested, what might be missing, and where failures occur is critical for maintaining quality. AI assistants can analyze your test repository and execution results to provide quick insights.

"Show me all test cases related to checkout and identify potential coverage gaps."

"Show me all failed tests from test run 'Release v1.25.0' from project 'Demo' and summarize the affected areas."

Assisted Test Automation Integration​

You can ask your AI assistant to help automate test result reporting:

"Mark all smoke tests in today's regression run as passed and add a summary comment."

or

"Create a new test run called 'Sprint 24 Regression Tests' in the Mobile App project and execute the test case TC-123"

The AI assistant will:

  1. Find the Mobile App project using listProjects
  2. Create the test run using createTestRun
  3. Try to execute the steps in the specified test case
  4. Update the test result using setTestResult
  5. Add comments using addTestResultComment
Prompt Hint

Always include enough context information for the assistant to be able to use e.g. a browser MCP server with URLs and test logins.

By combining Testiny's test management capabilities with AI assistants, teams can spend less time on repetitive administration and more time improving test quality.

Code Generation Example​

Generate test automation code based on your Testiny test cases:

"Generate Selenium WebDriver test code for test case TC-456 in Java."

The AI assistant will:

  1. Retrieve the test case details using listTestCases
  2. Analyze the test steps and expected results
  3. Generate appropriate automation code
  4. Suggest how to integrate with your test reporting using setTestResult
Prompt Hint

Include information about specific libraries, code style and existing test structure like page objects in your prompt.

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