How to Use the Curve Fitting Engine MCP in Windsurf

Q: Where do I configure the Curve Fitting Engine inside Windsurf?

Open your /.codeium/windsurf/mcpconfig.json file and add the server under the mcpServers key. You can also use the Windsurf settings UI to connect it in seconds.

Let Cascade run linear and polynomial regressions on your local datasets and write the math straight into your Windsurf code.

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Connect Curve Fitting Engine MCP to Windsurf

Create your Vinkius account to connect Curve Fitting 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 Curve Fitting Engine with Windsurf

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Perplexity

Let Cascade feed scatter data to this MCP Server

Stop writing boilerplate numpy code just to find a trendline. When Cascade sees a raw CSV or a scatter plot dataset in your workspace, it automatically calls `calculate_regression` to pull the exact coefficients. You get the math back without writing a single line of Python yourself. Cascade doesn't just stop at the raw numbers. Because it chains actions, it takes the resulting R-squared score and writes a clean plotting function right into your active Windsurf editor.

Chain complex regressions in Windsurf

When your data isn't linear, Cascade uses the `calculate_regression` tool to test different polynomial degrees. It loops through quadratic and cubic fits until it finds the best R-squared score. This MCP Server handles the heavy math locally in a fast V8 sandbox. Your agent reviews the fit, spots the trend, and updates your local dashboard code in one smooth motion.

Zero-friction math in your Windsurf workspace

No more copying coordinates into external math software. This MCP Server runs right inside your IDE, letting Cascade parse local arrays instantly with `calculate_regression`. You get the exact mathematical equation back in milliseconds. Cascade then drops those coefficients directly into your React charts or Python scripts.

Setup guide

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

mcp_config.json

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

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Common questions about Curve Fitting Engine MCP in Windsurf

Cascade spots data arrays in your files and passes them directly to `calculate_regression`. It then writes the resulting formula back into your code without you prompting every step.

Yes. This tool calculates polynomial regressions alongside standard linear fits. Cascade handles the degree selection based on your data shape.

Open your `~/.codeium/windsurf/mcp_config.json` file and add the server under the mcpServers key. You can also use the Windsurf settings UI to connect it in seconds.

No. The server runs self-contained math routines to calculate regressions. It doesn't need heavy local environments or extra package installs.

Your raw coordinate pairs and scatter plot data never leave your machine. Vinkius runs the server in an isolated local sandbox, ensuring your proprietary math stays completely private.

Use it with your favorite AI tools

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