How to Use the Matillion (Cloud Data Integration & ELT) MCP in Google ADK

Q: Can I restrict which Matillion (Cloud Data Integration & ELT) tools my Google ADK agent sees?

Use the toolnames filter in your toolset definition. This lets you limit the agent to specific functions like listprojects.

Query your ETL metadata and pipeline status right inside your Google ADK agent for enterprise-grade visibility.

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Connect Matillion (Cloud Data Integration & ELT) MCP to Google ADK

Create your Vinkius account to connect Matillion (Cloud Data Integration & ELT) 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 Matillion (Cloud Data Integration & ELT) with Google ADK

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Audit Matillion pipelines with Google ADK

Use `list_pipelines` to get a live inventory of your ETL tasks. Your agent can now reason over your entire data warehouse architecture. This visibility allows your agent to suggest optimizations based on actual runtime performance. You bridge the gap between raw data and actionable intelligence.

Monitor runtime agents and environments

Invoke `list_agents` and `list_environments` to verify that your infrastructure is online. Your agent keeps a close eye on the health of your deployment. This avoids the silent failures that usually plague complex data stacks. You get clear, verifiable status updates straight from the source.

Examine execution history with MCP

Retrieve granular execution logs using `list_executions`. If a job stalls, your agent has the data to pinpoint exactly where the process broke. This turns your agent into a technical auditor. It knows exactly which environment caused the bottleneck without requiring you to switch windows.

Setup guide

Set up Matillion (Cloud Data Integration & ELT) 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 Matillion (Cloud Data Integration & ELT) 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="Matillion (Cloud Data Integration & ELT)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Matillion (Cloud Data Integration & ELT) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Matillion (Cloud Data Integration & ELT) MCP in Google ADK

Initialize an McpToolset with your server URL. Add this toolset to your LlmAgent configuration to expose the server functions.

Yes. You can ingest the output of these tools directly into your Gemini model's context window for deep analysis of large-scale ETL jobs.

Use the tool_names filter in your toolset definition. This lets you limit the agent to specific functions like `list_projects`.

Your client communicates directly with the Vinkius-hosted server. The infrastructure is isolated and ephemeral for every session.

We enforce strict zero-trust protocols. Only non-sensitive execution metadata is transmitted, ensuring your proprietary pipeline logic remains private.

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