How to Use the Judge.me MCP in Google ADK

Q: Can Google ADK agents filter which Judge.me tools they use?

Yes. You can use the toolnames filter when setting up the MCP toolset. This restricts the agent to specific endpoints like listproducts while blocking access to others.

Q: Can my Google ADK agent write Judge.me data to BigQuery?

Yes, it can. Once the agent pulls customer feedback via listreviews, you use native Google Cloud integrations to structure and store that data in BigQuery.

Q: What are the data privacy implications of analyzing store feedback?

Pulling records via listanswers and listquestions exposes user account IDs and raw inquiry text to your Gemini models. You must ensure your Google Cloud project complies with your data retention policies and does not train foundational models on your private customer data.

Feed Judge.me product reviews into your Google ADK agents to run deep sentiment analysis across massive Gemini context windows.

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Connect Judge.me MCP to Google ADK

Create your Vinkius account to connect Judge.me 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.

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Setup Judge.me with Google ADK

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Process Massive Review Datasets

E-commerce stores generate thousands of reviews. You can dump all of them into Gemini's massive context window. The agent calls `list_reviews` via the MCP Server to pull raw customer feedback and aggregates the sentiment data directly. If you need to drill down into a specific complaint, the agent fires `get_review`. Because this framework integrates natively with your existing cloud infrastructure, you can pipe these insights straight into BigQuery for your analytics team.

Analyze Media via MCP Server

Text reviews only tell half the story. Customers upload photos and videos that show exactly how products fail in the real world. Your agent uses `list_medias` to grab these files and analyzes them using Gemini's multimodal capabilities. It cross-references visual defects with specific items by calling `get_product`. You find out exactly which manufacturing batch caused the issue. No more guessing why a specific SKU suddenly spiked in return rates.

Audit Q&A and Store Configuration

Unanswered questions sit on your product pages and kill trust. Your Gemini agent sweeps `list_questions` to find stale inquiries. It drafts technical responses based on your internal documentation. You also need to know if your review widgets are actually working. The agent runs `get_settings` to verify your store's email triggers and display configurations. If something breaks, the agent flags it before your marketing team even notices.

Setup guide

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

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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 Judge.me. 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 Judge.me MCP in Google ADK

Install the package. Initialize an McpToolset using StreamableHttpServerParameters with your endpoint URL. Pass that toolset directly to your LlmAgent.

Yes. You can use the tool_names filter when setting up the MCP toolset. This restricts the agent to specific endpoints like `list_products` while blocking access to others.

The framework supports both HTTP and standard input/output transports. For cloud deployments, HTTP is usually the easiest way to connect your agent to external services.

Yes, it can. Once the agent pulls customer feedback via `list_reviews`, you use native Google Cloud integrations to structure and store that data in BigQuery.

Pulling records via `list_answers` and `list_questions` exposes user account IDs and raw inquiry text to your Gemini models. You must ensure your Google Cloud project complies with your data retention policies and does not train foundational models on your private customer data.

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