How to Use the K-Means Cluster Engine MCP in OpenAI Agents SDK

Q: How do I connect the K-Means Cluster Engine to the OpenAI Agents SDK?

Initialize MCPServerStreamableHttp with your endpoint URL. Pass it into the mcpservers list when constructing your agent. The SDK auto-discovers the tools.

Group high-dimensional datasets deterministically using the OpenAI Agents SDK and this MCP Server.

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Connect K-Means Cluster Engine MCP to OpenAI Agents SDK

Create your Vinkius account to connect K-Means Cluster Engine to OpenAI Agents SDK 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 OpenAI Agents SDK

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Deterministic Clustering via OpenAI Agents SDK

The `calculate_kmeans` tool executes Euclidean distance classification directly on numerical matrices you feed it. You specify the target K, and the engine partitions the data points into fixed centroids. This gives your agent a strict mathematical grouping mechanism instead of relying on fuzzy probabilistic guesses. When you pass this tool to your agent via the HTTP transport, the built-in guardrails validate the inputs before execution. Your handoff workflows can route raw data to a specialized analysis agent, run the math, and pass the resulting centroid assignments down the pipeline.

High-Speed Vector Grouping

The `calculate_kmeans` operation processes multi-dimensional arrays without locking up your primary application thread. It calculates variance minimization across the dataset to find the most optimal rigid boundaries. You get fast, repeatable outputs every single run. Because the dashboard traces every call, you can monitor exactly how long the clustering takes. Set `cacheToolsList=True` during initialization to skip the discovery phase on subsequent runs. That drops overhead and keeps your production agents moving quickly through heavy workloads.

Strict Mathematical Bounds

The `calculate_kmeans` endpoint forces categorical or raw numerical data into structured groupings based on pure Euclidean geometry. There is no guessing involved. It assigns every single coordinate to the nearest mean and iterates until the assignments stop changing. That deterministic nature is exactly what production systems need. When your agent makes a decision based on user segmentation, you know exactly why a specific user landed in a specific cluster. The math guarantees a predictable outcome.

Setup guide

Set up K-Means Cluster Engine MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all K-Means Cluster Engine tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives K-Means Cluster Engine tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate K-Means Cluster Engine tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="K-Means Cluster Engine Agent",
            instructions="You have access to K-Means Cluster Engine tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about K-Means Cluster Engine MCP in OpenAI Agents SDK

Initialize `MCPServerStreamableHttp` with your endpoint URL. Pass it into the `mcp_servers` list when constructing your agent. The SDK auto-discovers the tools.

Only if you embed them first. The engine requires numerical arrays. Convert your text to vectors, then pass those arrays to the tool.

The tool rejects the request immediately. The built-in guardrails catch the error, allowing your agent to correct the parameters and retry.

Yes. You can assign the server to a specific data-processing agent. That agent handles the math and hands the clustered results off to your reporting agents.

The server processes your numerical coordinate matrices in a V8 Isolate Sandbox. It calculates the centroids, returns the assignments, and destroys the memory state immediately. Nothing persists after the connection closes.

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