How to Use the K-Means Cluster Engine MCP in Claude Code

Q: Does this tool require pre-scaled datasets?

Yes. The calculatekmeans tool relies on Euclidean distance, so you should have your agent scale the input metrics first to ensure accurate clustering results.

Pipe coordinate datasets directly into the K-Means Cluster Engine via Claude Code for headless terminal MCP operations.

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Connect K-Means Cluster Engine MCP to Claude Code

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Setup K-Means Cluster Engine with Claude Code

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Run `calculate_kmeans` directly from your terminal

The `calculate_kmeans` tool lets you perform high-speed mathematical clustering directly from your shell interface. Claude Code executes the tool to process raw coordinate arrays piped from your command-line utilities. This headless design allows you to run clustering jobs without launching an IDE or a heavy notebook environment. The terminal agent handles the input formatting and outputs clean JSON arrays of centroids and cluster indices.

Headless pipeline automation with Claude Code and this MCP Server

The `calculate_kmeans` tool integrates into your shell scripts and cron jobs through the Claude Code CLI. You can automate nightly data runs by instructing the CLI to pull data from a database, run the classification, and log the results. Because the server uses standard input/output protocols, it fits into your existing terminal toolchain. Your agent can pipe the mathematical outputs directly to other command-line utilities for further processing.

Rapid prototyping of vector classifications

The `calculate_kmeans` tool processes multi-dimensional arrays instantly, making it perfect for rapid terminal-based testing. Claude Code reads your raw log files, extracts numerical metrics, and groups them using Euclidean distance formulas. This setup allows SREs and backend devs to analyze server metrics or traffic patterns on the fly. You get immediate mathematical insights without writing complex scripts or leaving your SSH session.

Setup guide

Set up K-Means Cluster Engine MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see k-means-cluster-engine-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest K-Means Cluster Engine transactions." It will automatically discover and invoke the available K-Means Cluster Engine tools.

Terminal

claude mcp add --transport http k-means-cluster-engine-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the K-Means Cluster Engine MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

{
  "mcpServers": {
    "k-means-cluster-engine-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about K-Means Cluster Engine MCP in Claude Code

Run the CLI command to register the MCP server. Claude Code will instantly add the `calculate_kmeans` tool to its command list.

Yes. You can instruct Claude Code to read outputs from standard terminal commands and pass those numerical arrays to the `calculate_kmeans` tool for instant clustering.

It allows Claude Code to perform deterministic Euclidean grouping on raw data metrics. The MCP integration delivers rapid clustering results directly to your terminal.

Yes. The `calculate_kmeans` tool relies on Euclidean distance, so you should have your agent scale the input metrics first to ensure accurate clustering results.

All mathematical clustering is performed inside an isolated, zero-trust V8 sandbox. This MCP server handles your raw numerical datasets ephemerally, meaning they are never stored or transmitted.

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