TestDino MCP
Overview

TestDino MCP Server

Integrate TestDino with AI assistants like Claude and Cursor to get intelligent insights about your Playwright test results, automated debugging, and AI-powered test analysis.

What is MCP?

Model Context Protocol (MCP) is an open standard introduced by Anthropic that enables AI models to interact with external tools and resources in a consistent way. It allows AI assistants to access real-time data and perform actions through standardized interfaces.

What is TestDino MCP Server?

TestDino MCP server is a context provider that connects your AI tools (Claude Desktop, Cursor, and other MCP-compatible clients) with your Playwright test results. It enables AI assistants to:

  • Analyze failed tests and identify root causes
  • Access test execution history and trends
  • Retrieve detailed error logs and debugging information
  • Upload local test results for analysis
  • Provide insights on test flakiness and performance

Quick start steps

1. Installation

Install the TestDino MCP server globally via npm:

bash
npm install -g testdino-mcp

Alternatively, use npx to run without installation (recommended for trying it out):

bash
npx -y testdino-mcp

2. Configuration

Configure TestDino MCP server with your preferred AI assistant:

2.1 Cursor IDE

  1. Open Cursor Settings → MCP → Enable MCP

  2. Locate your mcp.json configuration file:

    • Windows: %APPDATA%\Cursor\mcp.json or .cursor/mcp.json in your project root
    • macOS/Linux: ~/.cursor/mcp.json or .cursor/mcp.json in your project root
  3. Add the TestDino MCP server configuration:

.cursor/mcp.json
{
  "mcpServers": {
    "TestDino": {
      "command": "npx",
      "args": ["-y", "testdino-mcp"],
      "env": {
        "TESTDINO_API_KEY": "your-api-key"
      }
    }
  }
}

For global installation:

.cursor/mcp.json
{
  "mcpServers": {
    "TestDino": {
      "command": "testdino-mcp",
      "env": {
        "TESTDINO_API_KEY": "your-api-key"
      }
    }
  }
}
  1. Restart Cursor completely
  2. Verify the installation by checking Settings → MCP - "TestDino" should appear in the server list
TestDino MCP Server configured in Cursor IDE

2.2 Claude Desktop

  1. Locate your Claude Desktop configuration file:

    • Windows: %APPDATA%\Claude\claude_desktop_config.json
    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Linux: ~/.config/Claude/claude_desktop_config.json
  2. Add the TestDino MCP server configuration:

claude_desktop_config.json
{
  "mcpServers": {
    "TestDino": {
      "command": "npx",
      "args": ["-y", "testdino-mcp"],
      "env": {
        "TESTDINO_API_KEY": "your-api-key"
      }
    }
  }
}

For global installation:

claude_desktop_config.json
{
  "mcpServers": {
    "TestDino": {
      "command": "testdino-mcp",
      "env": {
        "TESTDINO_API_KEY": "your-api-key"
      }
    }
  }
}
  1. Restart Claude Desktop to apply the configuration

3. Get Your TestDino API Key

To authenticate the MCP server with TestDino:

  1. Log in to your TestDino account (opens in a new tab)
  2. Navigate to Project Settings → API Keys
  3. Generate a new API key or copy an existing one
  4. Replace your-api-key in the configuration files above with your actual API key

Keep your API key secure and never commit it to version control. Consider using environment variables for production environments.

4. Example AI Prompts

Once configured, you can use natural language prompts with your AI assistant:

Check TestDino health

Show me all failed tests from the last run

Upload my latest Playwright test results

What are the most flaky tests from the last 7 days?

Analyze the failures in test run #12345

Features & Capabilities

The TestDino MCP server provides AI assistants with powerful tools to analyze and debug your Playwright tests:

Available Tools

ToolDescription
healthVerifies server availability and validates API key configuration
list_testrunsRetrieves test runs with advanced filtering by branch, time range, author, commit hash, and environment
get_run_detailsReturns comprehensive details of a specific test run, including pass/fail statistics and failure analysis
list_testcaseLists test cases with filtering by status, browser, error category, flakiness, and custom attributes
get_testcase_detailsRetrieves complete information about a specific test case, including error traces, screenshots, videos, and execution logs
upload_latest_local_test_runsUploads Playwright test results directly from your local development environment to TestDino for analysis

What AI Assistants Can Do

With access to these tools, AI assistants can help you:

  • Debug Test Failures: Analyze error messages, stack traces, and execution logs to identify root causes
  • Monitor Test Health: Track test stability, flakiness rates, and performance trends over time
  • Generate Reports: Summarize test run results and highlight critical issues
  • Optimize Tests: Identify slow tests, flaky tests, and tests that need attention
  • Upload Results: Push local test results to TestDino for centralized tracking and analysis
  • Historical Analysis: Compare test performance across different commits, branches, and environments

Common Use Cases

Ask your AI assistant to help with tasks like:

  • "Please fix this flaky test"
  • "Summarize my last test run and highlight critical failures"
  • "What were the top 5 flaky tests in the last 30 days?"
  • "Which test specs took the longest to run in the last week?"
  • "Show me all failed tests from the develop branch"
  • "Upload my latest Playwright test results to TestDino"
  • "Compare test performance between the main and feature branch"
  • "What's causing the authentication tests to fail?"

Next Steps

Ready to get started? Install the MCP server and start chatting with your AI assistant about your Playwright tests!