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The Spur MCP in action
MCP (Model Context Protocol) is a way to give your AI assistants and agents access to external tools and data — so they can take actions on your behalf, not just answer questions. With Spur’s MCP server, your AI assistant can run tests, analyze results, and help you debug failures — all without leaving your editor.
Using the Spur MCP alongside other MCP tools

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Common Workflows

Discover and Run Tests

“Run any tests I have for checkout”The agent finds matching tests and lets you run them directly from the conversation.See this in action

Analyze Failed Tests

“What went wrong in this run?”The agent reviews step results, console logs, and screenshots to surface the root cause.

Summarize Test Coverage

“What is missing from my checkout flow test coverage?”Your agent is provided with information about all your tests across suites and environments to identify gaps.

Fix Raised Bugs

“Help me fix the bug from this failed test”The agent pulls failure details, logs, and context to help you resolve issues in your code.See this in action

Connecting with Other MCP Tools

“Run the Spur test that covers this Jira ticket and this GitHub PR”Connect other tools, providing additional context to optimize your testing. Let your AI agent decide what to test.See this in action

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Available Tools

list_tests – Lists all tests in the active application with IDs, suites, and descriptionsget_test_details – Returns the full definition of a test: steps, environments, viewports, and scenario rows
run_test – Runs a single test by test_id and env_id. Optionally specify browser, viewport, or scenario rowrun_tests – Runs multiple tests in one collection run, grouped by shared configuration
get_test_run_overview – Start here. Summarizes a run’s status, step results, warnings, and failuresget_test_run_details – Deep dive into steps, sub-steps, configs, and artifacts. Use after the overviewget_test_runs – Lists the last 50 runs for a given test
get_test_run_console_logs – Browser console output and JavaScript errors (last 100 entries)get_test_run_network_logs – HTTP requests and responses captured during the run (last 5 entries)get_test_run_screenshots – Screenshots from the test execution for visual inspection
list_applications – Lists all applications on your account and shows the active oneswitch_application – Switches the active application for subsequent calls

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How to Set Up

Setup Instructions

Get started with Cursor, Claude Code, GitHub Copilot in VS Code, or ChatGPT.
 Authentication uses OAuth. When you connect for the first time, your browser will open to authorize access to your Spur account.

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Best Practices

  • Allow all tool calls except test execution: Set your MCP client to auto-approve all Spur tool calls except run_test and run_tests. This lets the agent freely discover tests, fetch details, and analyze results without interruption — while keeping you in control of when tests actually run.
  • Model quality matters: More capable models produce better results when choosing the right tools and interpreting test output. Smaller models may need more explicit guidance.