How to Use the Trengo MCP in Google ADK

Q: What data types does Trengo touch when using Google ADK?

It handles both structured account details (from getaccountprofile) and unstructured message content found via listmessages. These are ideal for BigQuery analysis.

Q: Can I list all team members using Trengo and Google ADK?

Yes. You use the listteammembers tool, which gives your agent a full roster of users in the system. This is key for routing complex support issues.

Q: What if I need to create a new ticket using Trengo and Google ADK?

You use the createticket tool call. The agent validates this action, allowing you to record new customer conversations directly into your managed system.

Q: How do I monitor webhooks using Trengo and Google ADK?

The agent allows you to use listwebhooks to see what triggers are set up. This is vital for maintaining accurate data pipelines feeding into your BigQuery tables.

Process enterprise communication data with Google ADK on the Trengo MCP Server.

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Connect Trengo MCP to Google ADK

Create your Vinkius account to connect Trengo 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 Trengo with Google ADK

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Manage ticketing and conversations

Your agent can read existing support threads using `list_tickets` or pull specific conversation history via `get_ticket`. Since it's running on Google Cloud, you can pass all this structured data directly into BigQuery for analysis. The system also lets you write new tickets with `create_ticket` and move status updates using `update_ticket`, ensuring your enterprise workflow stays accurate.

Use the MCP Server to manage contacts

The agent can list all communication channels via `list_channels` or get basic account info with `get_account_profile`. This helps Gemini models understand the full scope of your client's presence across multiple platforms. It also supports listing existing webhooks and triggering new ones using `create_webhook`, making integration reliable for massive datasets.

Automate messaging through the MCP Server

You can automate outreach by sending a direct message via `send_message`. For internal visibility, your agent pulls team details with `list_team_members` and gets contact lists using `list_contacts`. This setup allows you to process complex messaging actions—like listing messages (`list_messages`)—and then run those results through long-context reasoning in Vertex AI.

Setup guide

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

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Common questions about Trengo MCP in Google ADK

The Google ADK passes the endpoint to your agent, which auto-discovers all tools on the Trengo MCP Server. This lets Gemini models access everything from `list_tickets` to `send_message` within a single context.

It handles both structured account details (from `get_account_profile`) and unstructured message content found via `list_messages`. These are ideal for BigQuery analysis.

Yes. You use the `list_team_members` tool, which gives your agent a full roster of users in the system. This is key for routing complex support issues.

You use the `create_ticket` tool call. The agent validates this action, allowing you to record new customer conversations directly into your managed system.

The agent allows you to use `list_webhooks` to see what triggers are set up. This is vital for maintaining accurate data pipelines feeding into your BigQuery tables.

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