Vinkius
K-Means Cluster Engine

K-Means Cluster Engine MCP for AI. Group complex inputs with math, not guesswork.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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K-Means Cluster Engine MCP on Cursor AI Code EditorK-Means Cluster Engine MCP on Claude Desktop AppK-Means Cluster Engine MCP on OpenAI Agents SDKK-Means Cluster Engine MCP on Visual Studio CodeK-Means Cluster Engine MCP on GitHub Copilot AI AgentK-Means Cluster Engine MCP on Google Gemini AIK-Means Cluster Engine MCP on Lovable AI DevelopmentK-Means Cluster Engine MCP on Mistral AI AgentsK-Means Cluster Engine MCP on Amazon AWS Bedrock

Connect to your AI in seconds.

K-Means Cluster Engine uses deterministic K-Means classification to group complex datasets like geolocations or user profiles into precise clusters. It runs a battle-tested algorithm that strictly assigns every data point to its optimal cluster and finds the center (centroid) of those groups, eliminating guesswork.

What your AI can do

Calculate kmeans

Runs the K-Means clustering algorithm on a dataset to group data points into clusters.

Identify optimal data groupings

The MCP groups complex inputs like user records or purchase histories into mathematically defined clusters.

Calculate cluster centers

It determines the precise central point (centroid) for each identified group, giving you a measurable reference location.

Segment data by distance metric

The system assigns every raw data point to its closest cluster using Euclidean distance calculations.

Included with Plan

Waiting for input…

AI Agent

K-Means Cluster Engine: 1 Tool Available

This collection provides one tool, `calculate_kmeans`, which runs the K-Means clustering algorithm to segment data points mathematically.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using K-Means Cluster Engine on Vinkius

Calculate Kmeans

Runs the K-Means clustering algorithm on a dataset to group data points into clusters.

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Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The K-Means Cluster Engine integration is available immediately — no restart needed.

Choose How to Get Started

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Segmenting complex data points without constant math headaches?

Today, if you want to segment a thousand geolocations or group customer profiles by behavior, the process is messy. You feed the data into one system, get a vague list of 'clusters,' and then you have to manually copy the centroids and validate the groupings in another spreadsheet. It's slow, and those clusters are often mathematically unstable.

With this MCP, your agent runs the full K-Means classification locally. You give it the data, and it gives you back every point assigned to its optimal cluster, plus the exact coordinates for each group center. The math is done right, instantly.

K-Means Cluster Engine: Get Deterministic Clusters with `calculate_kmeans`

The main manual step that vanishes is the need for post-processing validation. You don't have to cross-reference cluster assignments or calculate centroids separately; the tool handles the entire rigorous mathematical assignment in one go.

Now, your workflow receives fully segmented data and precise center points immediately. It’s reliable math you can build into production agents.

What your AI can actually do with this

Pattern recognition needs math, not guesses. When you ask your agent to group thousands of coordinates or customer records using general language models, the results are usually unstable. This MCP changes that. It runs a reliable K-Means clustering algorithm locally within your autonomous workflows. You feed it raw data, and it calculates exactly where every point belongs.

The engine identifies cluster centers and reliably segments your data for tasks like finding geographic hotspots or separating normal user behavior from anomalies. Accessing this power through the Vinkius catalog makes sure you can integrate deterministic math into any client—Claude, Cursor, Windsurf, or whatever agent you use.

Built · Hosted · Managed by Vinkius K-Means Cluster Engine - Group Data Points & Centroids
Server ID 019e38b4-1493-701d-9580-b3ff28bb7c2a
Vinkius Inspector
Compliance Grade F
Score 3.6/100
Vinkius Inspector Badge — Score 3.6/100

Questions you might have

Is the clustering process fully deterministic? +

Yes, it guarantees consistent, mathematically precise assignments for every execution, completely avoiding LLM hallucination.

What kind of distance metric is used? +

The engine leverages standard Euclidean distance measurement, making it highly effective for uniform, continuous numeric datasets.

How fast is the data processing? +

Native execution within the Vinkius Edge runtime ensures that thousands of rows are fully clustered in mere milliseconds.

What type of data must I provide to the `calculate_kmeans` tool? +

The tool requires a structured, numerical dataset where every dimension represents a feature. You'll need to ensure your input array contains only quantifiable values for clustering to run correctly.

How does the K-Means Cluster Engine handle geographic coordinates? +

You can use this MCP for spatial routing and geographical segmentation by treating latitude and longitude as standard numerical dimensions. It accurately clusters raw delivery or location coordinates into defined zones.

Does running `calculate_kmeans` require external API keys or internet access? +

No, the engine runs entirely locally, which means it doesn't need external API calls or credentials. This keeps your data processing private and free from network friction.

What specific information does `calculate_kmeans` return after grouping points? +

The output provides the complete assignment of every input point to its optimal cluster, plus the calculated central coordinates (centroids) for each group. You get both membership and the center point.

Are there limitations on the size or complexity of data I can pass through the K-Means Cluster Engine? +

While designed for large datasets, extremely massive inputs may require chunking. For most common use cases involving thousands of records and a manageable number of dimensions, the engine performs quickly.

Built & Managed by Vinkius 30s setup 1 tools

We've already built the connector for K-Means Cluster Engine. Just plug in your AI agents and start using Vinkius.

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All 1 tools are live and waiting. You're up and running in seconds.

Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
Vinkius runs on Gemini Gemini
Vinkius runs on Windsurf Windsurf
Vinkius runs on VS Code VS Code
Vinkius runs on JetBrains JetBrains
Vinkius runs on Vercel Vercel
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