How to Use the Groove MCP in Google ADK

Q: Can I restrict Google ADK from updating Groove tickets?

Yes, you can use the toolnames filter when setting up your toolset. This prevents the Gemini model from calling tools like updateticketstate or createmessage.

Q: Does Groove work with Google ADK when handling Gemini's long-context window?

Absolutely, you can feed massive ticket histories from listmessages directly into the model. The Google ADK handles the token overhead without breaking the connection.

Connect Gemini models to Groove using Google ADK to analyze support history and resolve tickets at scale.

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Connect Groove MCP to Google ADK

Create your Vinkius account to connect Groove 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 Groove with Google ADK

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High-volume ticket analysis using Google ADK

Gemini models ingest thousands of support threads by calling `list_tickets` and `list_messages` inside the Google ADK environment. The model's long-context window processes years of customer interactions in a single step to identify recurring product bugs. Your agent then maps these trends directly to BigQuery tables using native cloud integrations. This setup gives your engineering team real-time visibility into support trends without manual exports.

Enterprise customer lookup via MCP Server

The server provides `get_customer` and `list_customers` to match incoming support requests with your existing Vertex AI customer profiles. Your agent instantly knows if a ticket comes from a high-value account. Once identified, the agent updates the ticket priority using `update_ticket_state` to escalate the issue. This keeps your enterprise customers happy by slashing response times for critical accounts.

Automated mailbox monitoring and dispatch

Your Gemini-powered agent uses `list_mailboxes` and `get_mailbox` to monitor incoming customer channels via the Groove MCP. The Google ADK handles the transport layer, letting the model process raw email text directly. When a new message arrives, the agent calls `create_ticket` to log the issue in the correct queue. This ensures zero customer emails slip through the cracks, even during peak support hours.

Setup guide

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

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Common questions about Groove MCP in Google ADK

Instantiate McpToolset with your Vinkius HTTP URL in your Python code. Pass that toolset into your LlmAgent constructor to expose the support tools to Gemini.

Yes, you can use the tool_names filter when setting up your toolset. This prevents the Gemini model from calling tools like update_ticket_state or create_message.

Absolutely, you can feed massive ticket histories from list_messages directly into the model. The Google ADK handles the token overhead without breaking the connection.

You track every tool execution through the Vertex AI dashboard. This gives you full visibility into when and why your agent called get_ticket or get_agent.

Yes, all customer emails and mailbox details are processed in zero-trust, ephemeral sandboxes on our MCP. No support data is ever used to train Google's foundation models.

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