How to Use the Matomo MCP in Google ADK

Q: How do I register the Matomo MCP tools inside my Google ADK agent?

Instantiate McpToolset using your Vinkius HTTP transport parameters. Pass this toolset directly to the LlmAgent constructor to expose endpoints like gettopwebsites to your Gemini model.

Q: How do I limit the Matomo tools exposed to my Google ADK agent?

Use the optional toolnames filter when defining your McpToolset in Python. This lets you restrict your agent to harmless read-only tools like listsites while blocking access to granular user profiles.

Feed self-hosted Matomo web analytics directly into your Google ADK enterprise agents for long-context Gemini reasoning.

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Connect Matomo MCP to Google ADK

Create your Vinkius account to connect Matomo 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 Matomo with Google ADK

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Process complete site histories in Google ADK.

The `get_visits_summary` tool retrieves high-level traffic metrics to feed the massive context window of your Gemini models. Your Google ADK agent parses months of raw visit data in a single pass to identify long-term traffic trends without chunking. Because Gemini handles up to one million tokens, you can pass entire summaries directly into the prompt. The framework correlates these web metrics with your existing BigQuery datasets to generate deep business intelligence.

Correlate referrer channels via Google ADK.

The `get_top_referrers` tool extracts incoming traffic channels directly into your enterprise agent workflows. Your Google ADK agent uses this data to compare web referral sources against your Google Cloud marketing databases. This direct integration lets you automate budget adjustments on ad platforms. The agent reads the referral breakdown, checks performance metrics, and updates cloud databases in real time.

Analyze top landing pages using this MCP Server.

The `get_top_pages` tool pulls your most visited URLs directly into the Gemini reasoning loop. Your Google ADK agent identifies underperforming landing pages by cross-referencing these URLs with bounce rates and goal completions. You can restrict which tools this MCP Server exposes using the `tool_names` filter in the ADK toolset configuration. This constraint ensures your agent only accesses specific content metrics while keeping administrative data isolated.

Setup guide

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

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Common questions about Matomo MCP in Google ADK

Instantiate `McpToolset` using your Vinkius HTTP transport parameters. Pass this toolset directly to the `LlmAgent` constructor to expose endpoints like `get_top_websites` to your Gemini model.

Yes, the ADK exposes the analytics tools to any model hosted on Vertex AI. Your model can query `get_top_socials` and use Vertex predictive tools to forecast social media traffic spikes.

Use the optional `tool_names` filter when defining your `McpToolset` in Python. This lets you restrict your agent to harmless read-only tools like `list_sites` while blocking access to granular user profiles.

Yes, the ADK supports both stdio and HTTP transports for tool execution. You can test your analytics queries locally before deploying the agent to Google Cloud Run.

Your raw visitor database queries and site details are processed in memory within your secure Google Cloud VPC. This MCP integration never writes your analytics payloads to persistent storage, keeping your compliance boundary intact.

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