How to Use the K-Means Cluster Engine MCP in Google ADK

Q: How do I integrate the K-Means Cluster Engine with the Google ADK?

Create an McpToolset pointing to your URL. Pass that toolset into the tools list of your LlmAgent.

Q: Does the Google ADK support filtering the K-Means tools?

It does. Use the toolnames parameter when initializing the toolset. You can restrict the agent to just the clustering endpoint if needed.

Run deterministic Euclidean partitioning on BigQuery data with the Google ADK.

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Connect K-Means Cluster Engine MCP to Google ADK

Create your Vinkius account to connect K-Means Cluster Engine 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 K-Means Cluster Engine with Google ADK

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Perplexity

Euclidean Partitioning via MCP Server

The `calculate_kmeans` tool applies rigid variance minimization to your numerical datasets. It takes an array of points and a target K, then iterates until the centroids stabilize. Your Gemini agents get hard mathematical boundaries instead of guessing. This matters when you pipe massive datasets from BigQuery into your agent's context. The framework handles the long-context memory, but this MCP Server handles the deterministic math. You get exact cluster assignments you can write straight back to your database.

Enterprise Data Segmentation

The `calculate_kmeans` endpoint processes high-dimensional vectors directly. It calculates the Euclidean distance between every point and the nearest mean. The algorithm stops only when the assignments lock into place. You connect it using `McpToolset` with `StreamableHttpServerParameters`. If your Vertex AI pipeline requires specific segmentation rules, you can use the `tool_names` filter to restrict your agent to just the clustering tool. It keeps the agent focused on the math.

Deterministic Centroid Mapping

The `calculate_kmeans` operation provides a strict grouping mechanism for continuous numerical metrics. You feed it coordinates, and it spits back cluster indices. The logic is entirely deterministic — run the same numbers, get the exact same groups. Gemini's massive context window means you can dump thousands of records into the prompt, but LLMs are terrible at arithmetic. Offloading the clustering to a dedicated engine ensures your enterprise data remains mathematically sound.

Setup guide

Set up K-Means Cluster Engine 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 K-Means Cluster Engine 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="K-Means Cluster Engine_agent",
    model="gemini-2.0-flash",
    instruction="You have access to K-Means Cluster Engine tools via MCP.",
    tools=mcp_tools,
)

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Common questions about K-Means Cluster Engine MCP in Google ADK

Create an `McpToolset` pointing to your URL. Pass that toolset into the `tools` list of your `LlmAgent`.

Yes. Pull your numerical columns from BigQuery, format them as arrays, and send them to the tool. The engine returns the cluster IDs for your agent to process.

It does. Use the `tool_names` parameter when initializing the toolset. You can restrict the agent to just the clustering endpoint if needed.

The math executes in milliseconds for standard matrices. If you send massive multi-gigabyte arrays, you might hit network limits before the algorithm chokes.

The engine runs your numerical vectors through a zero-trust, ephemeral sandbox. It calculates the Euclidean distances, outputs the centroid mapping, and wipes the instance. Your proprietary metrics are never stored.

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