How to Use the Umami Cloud MCP in Google ADK

Q: Can I limit which Umami Cloud tools are exposed to my agent using the Google ADK?

Yes. You can use the optional toolnames filter when setting up your McpToolset, restricting what the agent sees to just users or websites.

Act on Umami Cloud web metrics inside your enterprise workflow using the Google ADK.

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Connect Umami Cloud MCP to Google ADK

Create your Vinkius account to connect Umami Cloud 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 Umami Cloud with Google ADK

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Process website metrics with Umami Cloud MCP Server.

When you call `websites`, your agent gets specific metrics like URLs, browsers, OS, and devices. You can pass this structured data directly into a long-context prompt for deep reasoning within Gemini. This integration allows your enterprise agents to analyze web trends by comparing the Umami Cloud output against millions of tokens of existing documentation in BigQuery.

Get active user counts and feed them into BigQuery analysis.

The `users` tool pulls a clean count of actively visiting users. Because you're using the Google ADK, this data can be immediately reasoned upon alongside other structured datasets already residing in your BigQuery environment. The agent handles the transformation, ensuring that raw user counts are available for complex financial or operational queries.

Build enterprise agents with Umami Cloud MCP Server.

The Google ADK is built for massive scale. You can use the `websites` tool to pull site data, and then have your agent hold that context—along with other 1M+ tokens of information—to generate comprehensive reports. This keeps all your web analytics data anchored within the trusted Google Cloud infrastructure.

Setup guide

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

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Common questions about Umami Cloud MCP in Google ADK

You pass the MCP toolset to your LlmAgent. The agent then uses the `websites` tool's output and structures it for context within Gemini, allowing you to act on the data.

The server provides specific metrics including URLs, browsers, OS, and devices. The Google ADK handles this output, enabling deep analysis across your enterprise data.

It is designed for that scale. Since the Google ADK supports 1M+ tokens, the web metrics pulled from Umami Cloud are never lost in the context window.

Yes. The server touches web analytics data, specifically user counts and website metrics (URLs, browsers, OS, devices). This output is perfectly suited for structured querying in BigQuery.

Yes. You can use the optional `tool_names` filter when setting up your McpToolset, restricting what the agent sees to just `users` or `websites`.

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