How to Use the Confusion Matrix Engine MCP in Windsurf

Q: How do I install the Confusion Matrix Engine on Windsurf?

Open your /.codeium/windsurf/mcpconfig.json file and add the server configuration under the mcpServers key. Alternatively, use the Windsurf settings UI under Cascade to add the server URL.

Get exact precision, recall, and F1 metrics directly inside Windsurf without letting Cascade hallucinate the math.

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Connect Confusion Matrix Engine MCP to Windsurf

Create your Vinkius account to connect Confusion Matrix 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 Confusion Matrix Engine with Windsurf

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Calculate Exact Model Metrics via MCP Server

The `calculate_confusion_matrix` tool runs locally to compute true positives, false positives, precision, recall, and F1-score from your raw label arrays. Instead of letting Cascade guess the math and hallucinate incorrect percentages, this tool executes exact deterministic calculations. You feed your validation outputs to the agent, and it immediately returns a structured breakdown. Windsurf chains this data into your evaluation reports without writing custom Python math scripts.

Automate Model Evaluation Chains in Windsurf

Cascade uses the `calculate_confusion_matrix` tool to analyze model outputs and immediately decide whether to retrain or deploy. You set the target F1-score, and Windsurf runs the evaluation loop autonomously. It reads your prediction arrays from test files, triggers the calculation, and writes the markdown report. You skip the manual step of writing numpy boilerplate code.

Verify Classifier Performance Without Leaving Cascade

This MCP server exposes the `calculate_confusion_matrix` tool to let Windsurf inspect your classification models on the fly. You don't need to spin up a Jupyter notebook just to check if your model is biased toward false positives. Cascade pulls the raw arrays from your workspace, feeds them to the engine, and formats the confusion matrix in your editor. It keeps your development flow fast and focused.

Setup guide

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

mcp_config.json

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

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Common questions about Confusion Matrix Engine MCP in Windsurf

Open your ~/.codeium/windsurf/mcp_config.json file and add the server configuration under the mcpServers key. Alternatively, use the Windsurf settings UI under Cascade to add the server URL.

Yes, Cascade automatically detects the tool once connected. It will plan and execute metric calculations whenever you ask it to evaluate classification results.

LLMs struggle with mental math and often hallucinate metrics when reading raw arrays. This engine guarantees exact mathematical outputs instantly, freeing Cascade from writing, debugging, and running temporary Python execution scripts.

Yes, the server accepts arrays of any label type to generate multi-class confusion matrices. Your AI client parses the structured output to identify specific class confusion patterns.

Your raw actual and predicted label arrays never leave your local environment. Vinkius runs the server in an isolated, secure sandbox, ensuring your proprietary model predictions remain completely private.

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