How to Use the Feature Scaler Engine MCP in Windsurf

Q: Can Feature Scaler Engine handle MinMax scaling in Windsurf?

Yes, the scalefeatures tool lets you toggle between Z-score standardization and MinMax scaling. Windsurf chains this math tool directly into your active data preparation workflow without any manual intervention.

Run mathematical scaling on raw numeric features right inside Windsurf without writing standard deviation boilerplate.

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Connect Feature Scaler Engine MCP to Windsurf

Create your Vinkius account to connect Feature Scaler 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.

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Setup Feature Scaler Engine with Windsurf

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Fast numerical scaling inside Windsurf

Cascade grabs your local dataset, analyzes the raw numbers, and runs the `scale_features` tool to normalize them instantly. You don't have to write custom pandas code or import scikit-learn just to check if Z-score scaling fixes your model convergence. The agent handles the entire sequence of loading the data, running the math locally in our sandbox, and outputting the scaled values. This keeps your training pipelines clean since the heavy lifting happens before your model training script even starts.

Auto-chain normalization for neural networks

Tell Cascade to prepare your dataset for training, and it will automatically invoke `scale_features` to apply MinMax normalization. It reads the numeric columns, executes the math, and feeds the clean output directly into your training loop. This MCP Server runs entirely offline within a secure V8 sandbox, meaning your raw metrics never leave your workspace. Cascade handles the multi-step execution while you focus on tweaking your neural network architecture.

Clean clustering prep with this MCP Server

K-means clustering fails when your features have wildly different scales. Cascade uses the `scale_features` tool to standardize your variance, saving you from skewed centroids and bad groupings. By configuring this server in your configuration file, you give Windsurf the exact tool it needs to format raw metrics on the fly. No manual math, no silent failures, just clean inputs for your clustering algorithms.

Setup guide

Set up Feature Scaler 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 Feature Scaler 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

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 Feature Scaler Engine payment history." If connected, Cascade will call the Feature Scaler Engine tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

{
  "mcpServers": {
    "feature-scaler-engine-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Feature Scaler Engine MCP in Windsurf

Open your configuration file and add the server under the mcpServers key. Alternatively, use the Settings UI under Cascade to paste your single endpoint token, then hit refresh to let Cascade auto-discover these MCP tools.

Yes, the `scale_features` tool lets you toggle between Z-score standardization and MinMax scaling. Windsurf chains this math tool directly into your active data preparation workflow without any manual intervention.

Zero-variance columns cause a division-by-zero error during standardization. The `scale_features` tool detects this issue early and alerts Windsurf so your agent can drop or handle those columns before writing them back to disk.

Ask Cascade to standardize your numeric columns. It will call the `scale_features` tool with the default Z-score parameter to shift your distribution's mean to zero and standard deviation to one.

Every numeric column you scale stays inside our secure, ephemeral V8 isolate sandbox. Your raw features are never saved, stored, or sent to external servers, keeping your proprietary dataset completely private.

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