Zephyr Scale MCP. Audit your entire test lifecycle with conversation.
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Zephyr Scale (SmartBear) MCP connects your AI agent directly to your Jira quality assurance platform. You can manage entire testing workflows through conversation, pulling detailed test cases, monitoring live execution failures, and auditing entire release cycles without clicking through dozens of tabs.
It gives you a single viewpoint into the health of your software.
What your AI agents can do
Get execution
Pulls every detail about a specific test execution, including its full status report.
Get test case
Retrieves the complete definition and objective for any given test case.
Get test cycle
Shows all data related to a specific group of tests run for one release or sprint.
Retrieve specific test case details, including preconditions and objectives, for deep inspection.
List all defined test plans or browse the organizational folders to understand the overall project scope.
View group test runs (test cycles) to track how many tests passed for a specific sprint or major release.
Get real-time status updates on individual test executions, showing pass/fail metrics and step-by-step progress.
List the configured environments (like Staging or Production) to ensure tests run against the correct target system.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Zephyr Scale (SmartBear) with 10 Tools
These tools allow you to list plans, track runs, get specific test details, and manage all aspects of the testing lifecycle via natural language commands.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Zephyr Scale (SmartBear) on Vinkius019d7628get execution
Pulls every detail about a specific test execution, including its full status report.
019d7628get test case
Retrieves the complete definition and objective for any given test case.
019d7628get test cycle
Shows all data related to a specific group of tests run for one release or sprint.
019d7628list environments
Lists every configured testing environment, such as Staging or Production, in your project.
019d7628list executions
Provides a list of all test executions that have occurred within the project.
019d7628list folders
Lists organizational folders, which can be for test cases, cycles, or plans.
019d7628list statuses
Shows all custom status labels used when running tests in the project.
019d7628list test cases
Lists every test case available in a specific Jira project key, along with their current status.
019d7628list test cycles
Provides a list of all defined test cycles for the entire Zephyr Scale project.
019d7628list test plans
Lists every high-level plan that governs how testing should proceed in the project.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
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- Publish to catalog or keep private
Make Your AI Do More
Start with Zephyr Scale (SmartBear), then connect any of our 4,900+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,900+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zephyr Scale (SmartBear). All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
The Messy Way of Tracking Test Results
Today, checking test status is manual torture. You start by finding the main project plan in Jira, then you have to find the specific cycle associated with that release. From there, you're looking at a list of executions, and for every single fail, you click into it—which loads another page—just to see which step failed and why.
With this MCP, you just talk to your agent. You ask about the 'Regression Q1' cycle status, and the system handles all that deep navigation behind the scenes. It pulls the necessary data from list_test_cycles and get_execution into one conversational answer.
Test Case Discovery with list_test_cases
Before, if you needed to check a specific test case key (like PROJ-T1), you had to navigate the folder structure manually or search through hundreds of results. It was slow and unreliable.
Now, simply ask your agent to list_test_cases for that project. It instantly returns keys, names, and statuses—giving you immediate, filtered visibility into everything available.
What you can do with this MCP connector
You're managing complex QA infrastructure, but getting a full picture means jumping between multiple dashboards—one for planning, one for environments, and another for results. This MCP lets your agent handle that coordination automatically. Need to check if a specific test case (like PROJ-T1) passed on the Staging environment? You just ask.
The system pulls all that data together immediately.
It’s about building an auditable path from initial plan to final report. This MCP manages everything: listing high-level plans, checking available environments, and tracking every single test run's progress. Because this entire process flows through the secure architecture of Vinkius, your credentials pass through a zero-trust proxy; that means your keys are only used in transit, never sitting on disk.
It keeps complex QA data flowing securely while giving you full visibility into what happens with every tool call.
019d7628-4919-7084-a8ac-3de4f558533f 
How Zephyr Scale MCP Works
- 1 Subscribe to this MCP and enter your Zephyr Scale API Token.
- 2 Connect your preferred AI client from that single connection point in Vinkius.
- 3 Ask the agent a natural language question, like 'What's the status of the regression cycle?'
The bottom line is you stop navigating complex Jira plugins and start talking directly to your QA data.
Who Is Zephyr Scale MCP For?
This MCP is for anyone who spends time correlating test results with project planning. It's the Test Manager tired of cross-referencing spreadsheets, the Developer needing immediate failure details, and the Release Manager who needs a single source of truth before deploying code.
Runs checks to find out exactly why an execution failed by calling get_execution() for detailed step-by-step failure reports.
Checks the progress of multiple test runs using list_test_cycles and list_test_plans without manually filtering through Jira dashboards.
Verifies that all required tests are covered by checking both list_environments for targets and list_statuses for project compliance before approval.
What Changes When You Connect
- Instant status checks: Instead of navigating to list_executions, you ask for the result. The agent runs get_execution and tells you if it passed or failed immediately.
- Pinpoint failures fast: When a test fails, calling get_test_case provides the full script details—preconditions, objectives—so developers don't have to hunt through documentation.
- Scope management: You can use list_test_plans and list_test_cycles together to see if a specific release has enough coverage before you even start coding.
- Environment certainty: Need to know where the test runs? Use list_environments to confirm Staging is active, preventing deployment errors before they happen.
- Contextual discovery: The agent can guide you through the structure using list_folders, helping you find specific work items without knowing their exact key.
Real-World Use Cases
Debugging a sudden failure
A developer notices an execution failed. Instead of manually finding the run ID and clicking into it, they ask the agent to get_execution for that ID. The agent pulls the step-by-step log right up front, pinpointing exactly which assertion failed.
Pre-release signoff
A Release Manager needs confirmation that all critical features are covered. They ask to list_test_cycles for 'Q3 Regression' and cross-reference the results with list_environments to confirm it ran on Production data.
Assessing project scope
A Test Manager needs to know what tests exist but haven't been run yet. They ask the agent to combine list_test_cases and list_test_plans to compare the potential coverage against current cycle results.
Auditing setup integrity
A QA Engineer needs to confirm if a new feature was tested in all necessary environments. They use list_environments to check for 'Staging' and then query list_test_cases against that environment target.
The Tradeoffs
Manual dashboard hopping
The user opens Jira, navigates to the Test Cases tab, copies keys, goes to Cycles, finds run IDs, then opens a separate status board for results. It takes 15 minutes and requires five different tabs.
→ Connect your agent via this MCP. You ask one question—'Show me the test case key PROJ-T2 status on Staging.' The agent uses list_test_cases, checks list_environments, and retrieves the answer in a single response.
When It Fits, When It Doesn't
Use this MCP if your testing process requires auditing data across multiple, sequential stages: planning (list_test_plans), environment setup (list_environments), execution (get_execution), and final reporting. You need visibility into the flow of test artifacts.
Don't use this if you just need to look up a single piece of static information that isn't tied to Jira, like fetching a user list from an HR system; for those cases, another MCP is better. If your goal is simple data retrieval, remember the real power here: chaining multiple MCPs together allows you to build a full automation—for example, combining this Zephyr Scale MCP with a messaging MCP to automatically notify Slack when get_execution fails.
Common Questions About Zephyr Scale MCP
How do I find out all the active environments using list_environments? +
The agent calls list_environments and gives you a clean list of every configured target environment (Staging, Production, etc.) so you know exactly where your tests are running.
What is the difference between list_test_cases and list_executions? +
list_test_cases shows the static definition of what a test is. get_execution shows the live, recorded result of that test when it actually ran.
Can I check progress using list_test_cycles? +
Yes. list_test_cycles groups related runs for major releases or sprints and tells you how many tests passed versus the total count in that specific cycle.
Is it safe to use my API token with this MCP? +
Yes, absolutely. When running through Vinkius, your credentials pass through a zero-trust proxy; they are used only for the data transfer and never stored on disk.
How do I check the organizational hierarchy of my tests using list_folders? +
It lists all folders within your project, showing the complete structure. This allows you to navigate test cases, cycles, and plans by type (TEST_CASE, TEST_CYCLE, or TEST_PLAN) without manually browsing through Jira.
What kind of detailed data does get_execution provide for a specific run? +
The tool pulls the full result set for an execution. You get step-by-step progress details, including success/failure status, time taken, and specific error messages from failed steps.
How does list_test_plans help me understand project scope? +
It lists high-level test plans. This view helps you verify the overall testing strategy for a release or feature before any actual cycles begin running.
What happens if I use get_test_case with an incorrect key? +
The MCP returns an error response indicating that the resource ID is invalid. This predictable failure structure lets your agent immediately signal that the test case key you provided doesn't exist in the project.
Can I check the specific test steps for a case through the agent? +
Yes. The get_test_case tool allows your AI agent to retrieve the full script for any test key, providing the exact sequence of steps, test data, and expected results defined in Zephyr Scale.
How do I see the progress of a specific test cycle via chat? +
Use the get_test_cycle tool. Provide the unique cycle key, and your agent will return the cycle status, planned dates, and high-level execution statistics to help you monitor testing progress.
Is it possible to see the results of individual test runs? +
Absolutely. Using the get_execution_details tool, your agent can retrieve step-by-step results for any execution, including comments and execution time, helping you identify exactly where a test failed.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.