How to Use the ContextQA MCP in Mastra AI

Q: How do I handle a failed ContextQA run in Mastra AI?

Use getexecution inside a Mastra AI step to check the ContextQA run state, then use conditional branching to retry or halt the workflow.

Q: Can Mastra AI automatically map environments for ContextQA?

Yes, your Mastra AI agent uses listenvironments to fetch target layers from ContextQA and dynamically route test jobs.

Q: How do I register ContextQA tools from the MCP Server inside a Mastra AI agent?

Instantiating the client with new MCPClient lets you spread ContextQA tool definitions directly into your Mastra AI agent's tool array.

Q: Does this MCP Server support human-in-the-loop approval for ContextQA runs in Mastra AI?

Yes, you can set requireToolApproval on critical ContextQA tools like triggerrun to require manual confirmation before Mastra AI launches tests.

Q: How does the MCP Server secure my ContextQA project UUID data in Mastra AI?

Your ContextQA project mapping UUIDs retrieved via getproject remain in your local Mastra AI runtime memory. Vinkius executes the server in an ephemeral sandbox, meaning no UUID or environment configuration is ever written to disk.

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Mastra AI Workflows for Self-Healing Tests

`get_execution` tracks the precise state of your ContextQA self-healing runs so your Mastra AI workflow can handle branching logic based on test failures. If a ContextQA test run fails, the Mastra AI workflow engine catches the state and triggers a retry or routes the issue to your team. The Mastra AI agent checks `get_case` to validate ContextQA data science object extraction boundaries before proceeding to the next step. This keeps your automated Mastra AI pipelines moving without stalling on minor ContextQA extraction errors.

Multi-Layer Environment Routing via Mastra

`list_environments` retrieves static environment target layers so your Mastra AI agent can dynamically choose where to deploy ContextQA test runs. The Mastra AI workflow automatically switches targets based on which ContextQA environment is ready. Using `list_projects`, your Mastra AI agent groups automated validations and maps ContextQA project UUIDs before executing any live commands. This ensures your Mastra AI workflows target the correct isolated ContextQA environment every single time.

Trigger ContextQA Runs via Mastra AI Steps

`trigger_run` dispatches live ContextQA testing commands directly from your Mastra AI workflow steps when a deployment completes. This allows you to chain code deployments and ContextQA test suites in a single automated Mastra AI process. If the ContextQA run fails, `list_suites` extracts the structural GUI test payloads to analyze the failure pattern inside your Mastra AI workflow. The Mastra AI agent then decides whether to roll back the deploy or alert your engineers about the ContextQA failure.

Setup guide

Set up ContextQA 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 ContextQA 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: "contextqa-mcp-client",
  servers: {
    "contextqa-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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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 ContextQA. 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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Common questions about ContextQA MCP in Mastra AI

Use `get_execution` inside a Mastra AI step to check the ContextQA run state, then use conditional branching to retry or halt the workflow.

Yes, your Mastra AI agent uses `list_environments` to fetch target layers from ContextQA and dynamically route test jobs.

Instantiating the client with `new MCPClient` lets you spread ContextQA tool definitions directly into your Mastra AI agent's tool array.

Yes, you can set `requireToolApproval` on critical ContextQA tools like `trigger_run` to require manual confirmation before Mastra AI launches tests.

Your ContextQA project mapping UUIDs retrieved via `get_project` remain in your local Mastra AI runtime memory. Vinkius executes the server in an ephemeral sandbox, meaning no UUID or environment configuration is ever written to disk.

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