How to Use the Zephyr Scale (SmartBear) MCP in Mastra AI

Q: Does Zephyr Scale (SmartBear) work well with Mastra AI for status tracking?

Yes. You can build a workflow that periodically calls liststatuses to track custom test execution statuses. If the status changes, your agent triggers the next step—like notifying a developer.

Build resilient, multi-step workflows handling failure with Mastra AI and MCP Server.

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Connect Zephyr Scale (SmartBear) MCP to Mastra AI

Create your Vinkius account to connect Zephyr Scale (SmartBear) to Mastra AI 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 Mastra AI

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Run Test Cycle Checks via Mastra AI

Mastra AI uses the `list_test_cycles` tool to fetch all test cycles in a Zephyr Scale (SmartBear) project. If your workflow fails on one step, the agent can automatically retry or branch using conditional logic, ensuring you don't lose context across multiple steps.

Identify Relevant Test Cases with Mastra AI

You need to know which test cases exist? The `list_test_cases` tool accepts a Jira project key and returns keys, names, and statuses. Mastra AI can use this data in complex branches—for instance, 'if status is Failed, then notify admin; else proceed.' This makes your workflows robust.

Check Project Environments with MCP Server

The `list_environments` tool lets the agent see all available environments like Staging or Production. Mastra AI can write a workflow that checks these environments first, and only proceeds to test execution if the correct environment flag is found. This prevents accidental deployment failures.

Setup guide

Set up Zephyr Scale (SmartBear) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Zephyr Scale (SmartBear) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "zephyr-scale-smartbear-mcp-client",
  servers: {
    "zephyr-scale-smartbear-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Zephyr Scale (SmartBear) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Zephyr Scale (SmartBear) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Zephyr Scale (SmartBear) transactions"
);
console.log(result.text);

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Common questions about Zephyr Scale (SmartBear) MCP in Mastra AI

Mastra AI treats the MCP Server as a reliable toolset. It can incorporate tools like `get_execution` into complex workflows, allowing it to check test results and make decisions based on success or failure status.

Absolutely. You can build a flow where the agent first calls `list_test_plans`, then uses those plans to call `get_test_case` details, and finally records that information in an external system. It handles failure at any point.

Yes. You can build a workflow that periodically calls `list_statuses` to track custom test execution statuses. If the status changes, your agent triggers the next step—like notifying a developer.

This server touches various records: test case details, execution lists, and environment names. The agent manages the flow across these different types of metadata.

Since it's designed for complex workflow automation, it provides built-in features like exponential backoff and retries. This makes it ideal for mission-critical testing operations.

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