How to Use the Zenloop MCP in Google ADK

Q: Can I list all surveys using Zenloop and Google ADK?

Yes. Calling listsurveys gives your agent a complete inventory of every survey configured in the Zenloop system, making discovery simple for your pipelines.

Q: What if I need to send a new answer using Zenloop and Google ADK?

You have multiple tools at your disposal. You can use createembedanswer, createlinkanswer, or the others, depending on whether the feedback originated from an embed, link, or overlay.

Q: Is filtering available when listing results with Zenloop and Google ADK?

Definitely. The listsurveyanswers tool allows you to filter responses by date. This granularity lets your agent focus its BigQuery queries on narrow, time-boxed datasets.

Process customer surveys in BigQuery using the Google ADK.

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Connect Zenloop MCP to Google ADK

Create your Vinkius account to connect Zenloop 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 Zenloop with Google ADK

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Handling Survey Responses

Your agent uses `list_survey_answers` to pull response data for any survey. The tool lets you filter these responses by date, keeping your analysis focused on specific time windows. When a new answer comes in—whether through an email embed (`create_email_answer`), website overlay (`create_overlay_answer`), or direct link (`create_link_answer`)—your agent writes it directly into the appropriate record.

Managing Survey Metadata

Want to check what surveys Zenloop is running? The `list_surveys` tool provides a full list of all configured campaigns. This gives your BigQuery pipeline visibility into every active survey. Need the deep details on one survey? Use `get_survey_details`. It pulls down comprehensive metadata, letting you understand the setup and rules for any given survey ID.

Confirming Account Status

Start by getting core credentials using the `get_account_details` tool. This verifies your Zenloop account information before any processing occurs. This confirms that the enterprise agent has the necessary context and permissions to write data, preventing failed jobs downstream in BigQuery.

Setup guide

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

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Common questions about Zenloop MCP in Google ADK

Your agent uses the MCP Server tools to gather raw survey answers. These structured responses feed into your Gemini models, allowing you to analyze sentiment within BigQuery.

Yes. Calling `list_surveys` gives your agent a complete inventory of every survey configured in the Zenloop system, making discovery simple for your pipelines.

You have multiple tools at your disposal. You can use `create_embed_answer`, `create_link_answer`, or the others, depending on whether the feedback originated from an embed, link, or overlay.

Definitely. The `list_survey_answers` tool allows you to filter responses by date. This granularity lets your agent focus its BigQuery queries on narrow, time-boxed datasets.

The server manages structured survey answers and account details. It specifically handles NPS feedback and the core records for all submitted surveys.

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