How to Use the Katalon TestOps (AI Test Management) MCP in Vercel AI SDK

Q: Can the Vercel AI SDK trigger test reruns in Katalon TestOps (AI Test Management)?

Yes. The framework invokes the reruntestrun tool when the user requests a retry. It returns the new run ID immediately to your frontend.

Q: How does Katalon TestOps (AI Test Management) handle streaming data in the Vercel AI SDK?

The AI client calls tools like listtestresults and streams the JSON response chunks directly to your application. Your React components render the test durations and statuses as they arrive.

Q: What do I need to query specific project builds via the MCP Server?

You supply the project ID to the listprojectbuilds tool. The server fetches the build names and test run counts, which the model then formats for your users.

Stream live Katalon test execution data directly into your React and Next.js applications using the Vercel AI SDK.

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Connect Katalon TestOps (AI Test Management) MCP to Vercel AI SDK

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Setup Katalon TestOps (AI Test Management) with Vercel AI SDK

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Stream Test Results with Vercel AI SDK

Fetching live testing data requires `list_test_runs` and `list_test_results` to pipe pass/fail metrics straight into your frontend. Your AI client streams these execution durations and error messages directly into your React components. Users watch the test suite progress in real-time instead of staring at a blank loading screen. This setup handles the heavy lifting of polling execution statuses. You just pass the MCP Server tool results into your `streamText` function. The UI updates instantly as individual tests complete. Your development team gets immediate feedback on broken builds.

Expose Release Metrics to End Users

Querying `list_project_releases` and `list_project_builds` pulls scheduled dates and aggregated test run statistics into your application state. Your Next.js interface immediately renders the health of an upcoming software release. The AI client formats these build identifiers and test counts on the fly. You avoid writing custom API wrappers for the Katalon endpoints. The integration feeds raw project data from `get_project` directly to the language model. Your users get a conversational interface that answers specific questions about release readiness based on live data.

Trigger Reruns from the UI

Executing `rerun_test_run` lets your users restart failed test suites straight from your custom dashboard. The AI client receives the newly created test run ID and immediately streams that confirmation back to the frontend. Nobody has to log into the Katalon portal to manage test execution. Combining this with `list_execution_environments` lets your application prompt users to select specific environments for their retries. The Vercel AI SDK handles the tool invocation securely from your Edge Functions. You build interactive, user-facing quality assurance tools with minimal backend code.

Setup guide

Set up Katalon TestOps (AI Test Management) MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Katalon TestOps (AI Test Management) tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Katalon TestOps (AI Test Management) transactions",
});

console.log(text);
await mcpClient.close();

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Katalon TestOps. 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 Katalon TestOps (AI Test Management) MCP in Vercel AI SDK

Install the @ai-sdk/mcp package and define your HTTP transport connection. Pass the resulting tool array into your generateText or streamText calls.

Yes. The framework invokes the rerun_test_run tool when the user requests a retry. It returns the new run ID immediately to your frontend.

The AI client calls tools like list_test_results and streams the JSON response chunks directly to your application. Your React components render the test durations and statuses as they arrive.

You supply the project ID to the list_project_builds tool. The server fetches the build names and test run counts, which the model then formats for your users.

This integration reads test execution durations, error logs, and release schedules from your Katalon projects. Vinkius runs the connection in an ephemeral V8 Isolate Sandbox, ensuring your proprietary QA metrics never leak across sessions.

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