How to Use the Katalon TestOps (AI Test Management) MCP in Google ADK

Q: How do I configure Katalon TestOps (AI Test Management) with Google ADK?

Install google-adk and set up an McpToolset using StreamableHttpServerParameters. Pass that toolset directly to your LlmAgent configuration.

Q: Can I limit which tools the agent uses in Katalon TestOps (AI Test Management) via Google ADK?

You pass a toolnames filter when initializing the toolset. This restricts the agent to specific read-only MCP tools if you want to prevent automated reruns.

Q: Does the agent know which test builds belong to which releases?

It calls listprojectbuilds and listprojectreleases to map build IDs to scheduled release dates. The agent correlates these statistics automatically.

Q: How do I handle test runs across multiple projects in Katalon TestOps (AI Test Management) using Google ADK?

The MCP integration queries listprojects to grab the necessary IDs, then iterates through them. Gemini's long context handles the aggregated data easily.

Connect your Gemini agents to Katalon TestOps using Google ADK to analyze test runs across massive context windows.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Katalon TestOps (AI Test Management) MCP to Google ADK

Create your Vinkius account to connect Katalon TestOps (AI Test Management) 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.

GDPR Free for Subscribers

Setup Katalon TestOps (AI Test Management) with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Map test environments with Google ADK

`list_execution_environments` and `list_projects` feed your Gemini agent the foundational structure of your QA setup. The agent pulls project names, IDs, and descriptions directly from Katalon TestOps. Gemini uses its massive token window to hold this entire environment map in memory. It cross-references your TestOps configurations against infrastructure data you already store in BigQuery.

Analyze massive test logs via MCP Server

`list_test_results` and `get_test_result` pull durations, statuses, and error messages into your agent's context. When a massive suite fails, the agent reads the exact failure points. You stop wasting time scrolling through endless CI/CD logs. The MCP Server feeds the raw execution data straight to your Google Cloud agent, which summarizes the root causes in seconds.

Trigger reruns from the cloud

`rerun_test_run` lets your agent restart failed executions on demand. It grabs the new run ID and monitors progress via `get_test_run` and `list_test_runs`. You define the logic in Vertex AI. If the agent detects a known flaky network timeout in the error messages, it automatically restarts the job without waking up an engineer.

Setup guide

Set up Katalon TestOps (AI Test Management) 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 Katalon TestOps (AI Test Management) 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="Katalon TestOps (AI Test Management)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Katalon TestOps (AI Test Management) tools via MCP.",
    tools=mcp_tools,
)

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Katalon TestOps (AI Test Management) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Katalon TestOps (AI Test Management) MCP in Google ADK

Install `google-adk` and set up an `McpToolset` using `StreamableHttpServerParameters`. Pass that toolset directly to your `LlmAgent` configuration.

You pass a `tool_names` filter when initializing the toolset. This restricts the agent to specific read-only MCP tools if you want to prevent automated reruns.

It calls `list_project_builds` and `list_project_releases` to map build IDs to scheduled release dates. The agent correlates these statistics automatically.

The MCP integration queries `list_projects` to grab the necessary IDs, then iterates through them. Gemini's long context handles the aggregated data easily.

The zero-trust architecture ensures your project descriptions and test run metrics remain isolated. The server operates in an ephemeral state and never persists your Katalon data after the request completes.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript