How to Use the PractiTest MCP in Google ADK

Q: Can Google ADK filter which PractiTest tools are exposed?

You can restrict access using the toolnames filter in your MCP toolset configuration. If you only want an agent to read data, just expose getproject and listtests while blocking the creation endpoints.

Give your Google ADK agents direct access to PractiTest. Query test runs, analyze requirements, and sync QA data with BigQuery.

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Connect PractiTest MCP to Google ADK

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Setup PractiTest with Google ADK

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Google ADK test log analysis

Gemini models can ingest massive context windows, making them perfect for analyzing historical QA data. Your agent calls `list_runs` to pull months of execution logs from PractiTest. It feeds that raw data into the model to spot flaky tests and recurring failure patterns. You can then pipe those insights straight into your Google Cloud infrastructure. The agent identifies problematic areas using `get_test` and writes the aggregated metrics into BigQuery for your data science team to analyze.

Automated requirement mapping via MCP Server

Keeping product specifications synced with test coverage is a constant headache. Your agent solves this by polling `list_requirements` to fetch the latest product definitions. It compares these specs against your active codebase to find gaps. When it finds a missing scenario, the agent takes action. It drafts the missing coverage and fires off a `create_test` request to PractiTest. Because Gemini handles large contexts so well, it can read your entire PR and generate the exact JSON payload needed.

Cross-project QA orchestration

Enterprise teams rarely work in a single workspace. Your agent uses `list_projects` to map out the entire organization's testing environments. It then runs `list_instances` across those projects to build a unified view of what is currently testing. If a critical bug drops in production, the agent reacts across the board. It triggers `create_instance` to queue up emergency regression suites in every affected project. This happens in seconds, entirely driven by the Google ADK framework.

Setup guide

Set up PractiTest 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 PractiTest 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="PractiTest_agent",
    model="gemini-2.0-flash",
    instruction="You have access to PractiTest tools via MCP.",
    tools=mcp_tools,
)

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Common questions about PractiTest MCP in Google ADK

Install google-adk and set up an McpToolset using StreamableHttpServerParameters. Pass that toolset to your LlmAgent initialization. The agent will immediately see the QA tools and understand how to use them.

You can restrict access using the tool_names filter in your MCP toolset configuration. If you only want an agent to read data, just expose get_project and list_tests while blocking the creation endpoints.

Agents can hit APIs fast, especially when iterating over list_runs. You should implement standard backoff logic in your Google Cloud environment to ensure the agent doesn't overwhelm your workspace quota.

The framework supports both Stdio and HTTP transports natively. Most enterprise users prefer the HTTP setup for centralized server management across their cloud environments.

Everything operates under a zero-trust architecture. The server processes your test instances, run statuses, and project IDs in memory only. No data is cached on disk, and the connection requires a single endpoint token for authentication.

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