How to Use the Zephyr Scale (SmartBear) MCP in Google ADK

Q: How do I get a list of all test executions with Zephyr Scale (SmartBear) and Google ADK?

You run listexecutions. This provides an overview of every historical test run, which is crucial for feeding data into Vertex AI.

Q: Does the MCP Server support finding all project artifacts?

Yes. You can list everything from environments (listenvironments) to plans (listtestplans), giving your agent a complete picture of the scope.

Q: What are the best practices for using this MCP Server?

Start by listing all environments (listenvironments) to make sure your agents know where they're operating before trying to read any test data. It keeps everything contained.

Act on your data with Google ADK. Run Zephyr Scale (SmartBear) tests inside your BigQuery pipeline.

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Connect Zephyr Scale (SmartBear) MCP to Google ADK

Create your Vinkius account to connect Zephyr Scale (SmartBear) to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Zephyr Scale (SmartBear) with Google ADK

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List all test runs and results

The `list_executions` tool pulls a list of every completed test run in the project. Then, `get_execution` lets you grab all the deep details for one specific run. This is key when your agent needs to analyze performance data from BigQuery.

Check status and folder structure

Use `list_statuses` to pull every custom test execution state. Similarly, `list_folders` helps you map out the entire project's directory structure by type (TEST_CASE, TEST_CYCLE, or TEST_PLAN). It gives your agent a clear view of how artifacts are organized.

Plan and group test cycles

Need to understand which tests belong together? `list_test_cycles` shows all the grouped runs for a specific release or sprint. Meanwhile, `get_test_case` pulls full details on any single test case you're investigating. This helps your enterprise agent manage complex releases.

Setup guide

Set up Zephyr Scale (SmartBear) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Zephyr Scale (SmartBear) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Zephyr Scale (SmartBear)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Zephyr Scale (SmartBear) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Zephyr Scale (SmartBear) MCP in Google ADK

You run `list_executions`. This provides an overview of every historical test run, which is crucial for feeding data into Vertex AI.

Yes. You can list everything from environments (`list_environments`) to plans (`list_test_plans`), giving your agent a complete picture of the scope.

It can. Since you're on Google Cloud, your agent naturally works alongside BigQuery and Vertex AI, treating test results as just another high-value dataset.

Start by listing all environments (`list_environments`) to make sure your agents know where they're operating before trying to read any test data. It keeps everything contained.

This server touches execution details, including full run results and the status history attached to those runs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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