How to Use the K-Means Cluster Engine MCP in Windsurf
Run multi-step vector grouping pipelines in Windsurf using this mathematical MCP Server to cluster high-dimensional data.
Works with every AI agent you already use
…and any MCP-compatible client
Connect K-Means Cluster Engine MCP to Windsurf
Create your Vinkius account to connect K-Means Cluster Engine to Windsurf and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Run `calculate_kmeans` inside Windsurf Cascade
The `calculate_kmeans` tool runs directly inside your IDE to partition coordinate datasets into distinct mathematical groups without manual scripts. Cascade reads your raw data files, feeds the coordinates to this tool, and instantly gets back the clustered coordinates with their assigned centroids. Because Cascade chains actions autonomously, it takes the output coordinates and immediately writes a Python script to visualize the clusters. You skip the manual setup of importing distance libraries because the engine handles the Euclidean distance calculations out of the box.
Autonomous cluster optimization via Windsurf and this MCP Server
The `calculate_kmeans` tool calculates precise cluster assignments, enabling Windsurf to run iterative elbow-method tests on your local datasets. Cascade triggers the tool across a range of K values, parses the inertia metrics, and determines the optimal number of groupings on its own. Your agent writes the configuration files based on these mathematical outputs. This eliminates the guesswork when setting up user segments or grouping geographic coordinates directly in your active workspace.
Local data preparation and partition modeling
The `calculate_kmeans` tool processes multi-dimensional numerical arrays right inside your local development environment. Windsurf loads your raw database exports, executes the classification tool, and organizes the unstructured vectors into structured outputs. This integration bypasses the need for heavy external machine learning frameworks during the prototyping phase. You get raw mathematical groupings back in milliseconds, allowing your agent to update your database schemas or API mock data instantly.
Set up K-Means Cluster Engine MCP in Windsurf
Prerequisites
- Windsurf IDE installed (macOS, Windows, or Linux)
- Active Vinkius subscription with a valid endpoint token
- 1
Open MCP configuration
Click the Cascade assistant icon in the sidebar, then click the hammer icon (🔨) at the top of the panel. Select "Configure" to open
~/.codeium/windsurf/mcp_config.json. - 2
Add the K-Means Cluster Engine MCP
Paste the JSON snippet shown on the right into the
mcpServersobject. Replace[YOUR_TOKEN_HERE]with your endpoint token from cloud.vinkius.com. - 3
Refresh MCPs
Go back to the hammer icon (🔨) in Cascade and click "Refresh". Windsurf will detect the new server. No full restart is needed — the connection is hot-reloaded.
- 4
Verify in Cascade
Start a new Cascade conversation and ask something like "Show my K-Means Cluster Engine payment history." If connected, Cascade will call the K-Means Cluster Engine tools directly. You will see a green dot next to the server name in the MCP panel.
{
"mcpServers": {
"k-means-cluster-engine-mcp": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ml-kmeans. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Common questions about K-Means Cluster Engine MCP in Windsurf
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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