How to Use the Medallia MCP in Google ADK

Q: How do I configure the Medallia MCP Server inside Google ADK?

Initialize the tools using McpToolset with your HTTP server parameters. Pass this toolset directly into your LlmAgent definition to give Gemini immediate access to customer surveys and alerts.

Connect Google ADK enterprise agents to Medallia via MCP to analyze millions of customer experience data points in Google Cloud.

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Connect Medallia MCP to Google ADK

Create your Vinkius account to connect Medallia 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 Medallia with Google ADK

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Bridge BigQuery data with Medallia insights

By calling `list_responses`, your Google ADK agent combines BigQuery database records with live Medallia customer feedback. It's a direct way to map feedback against customer profiles stored in Google Cloud. This setup lets you run deep-dive analyses on customer churn using Vertex AI. By using this MCP Server, your Gemini-powered agent can process thousands of Medallia feedback rows at once, giving you a clear picture of user sentiment.

Track program health using Google ADK and Gemini

Using the `list_programs` tool, your Google ADK agent takes advantage of Gemini's massive token limits to scan entire Medallia experience management programs. It'll call `get_program_details` to ingest the entire program structure in one go. With this context, the Google ADK agent can analyze long-term trends across multiple customer touchpoints. It uses `list_surveys` to monitor active Medallia questionnaires and flags systemic issues directly to your team.

Automated enterprise alert response loops

By querying `list_alerts`, your Google ADK agent tracks and closes the loop on active customer complaints. It'll extract the exact pain points using `get_alert` without you writing custom parsing code. Because the Google ADK integrates with Vertex AI, your agent can draft highly personalized response emails based on the Medallia alert details. It can also query `list_users` to assign the follow-up task to the right account manager.

Setup guide

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

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Common questions about Medallia MCP in Google ADK

Initialize the tools using `McpToolset` with your HTTP server parameters. Pass this toolset directly into your `LlmAgent` definition to give Gemini immediate access to customer surveys and alerts.

Yes, Gemini's long-context window allows the agent to ingest hundreds of rows from `list_responses` at once. The agent can then cross-reference these responses with your enterprise data in BigQuery.

Yes, you can use the optional tool names filter in the toolset configuration. This allows you to expose only `get_response` and `search_responses` while hiding user management tools.

The server supports both Stdio and HTTP transports. For Google Cloud deployments, the streamable HTTP transport is recommended to maintain reliable, persistent connections between your agent and the API.

All customer experience data, including surveys and user lists, remains strictly inside your secure Google Cloud VPC and the Vinkius sandbox. No data is cached or stored permanently on the MCP server.

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