• METRICS
  • MATRIX
  • ANALYSIS
  • HUMAN

How to calculate model performance metrics with your AI

Connect this MCP to your AI client to turn raw prediction arrays into hard numbers. It handles the math so you don't have to.

Ask AI about this page

Short answer

How can I get precision and recall from my model's labels?

Your AI uses a deterministic runtime to process raw label arrays and output specific metrics like precision, recall, and F1-score. You get exact statistical values instead of probabilistic guesses. This breakdown shows how the math works.

The outcomes.

What you get from the calculation.

Once you provide the data, your AI produces these specific outputs.

METRICS

Exact score values

The AI calculates precision, recall, and F1-score from your arrays. You get the specific numbers needed for your report.

MATRIX

Confusion matrix data

The engine maps out true positives, false positives, true negatives, and false negatives. This shows exactly where your model is tripping up.

ANALYSIS

Error distribution

The AI identifies which classes are being misclassified most often. This helps you see if your model has a specific bias.

HUMAN

Manual review needed

The AI provides the numbers, but you decide if the model's performance meets your project's requirements. You interpret the meaning behind the scores.

The workflow

What your AI does when the data arrives.

The AI moves from raw data to statistical certainty through a structured process.

  1. Data ingestion

    Your AI takes your raw arrays of true labels and predicted labels and prepares them for processing.

    calculate_confusion_matrix

  2. Matrix computation

    The engine runs a deterministic calculation to build the confusion matrix. This ensures the math is perfect every time.

    calculate_confusion_matrix

  3. Metric derivation

    The AI pulls precision, recall, and F1-score directly from the matrix results.

    calculate_confusion_matrix

  4. Summary generation

    Your agent summarizes the findings into a readable format for your review.

    calculate_confusion_matrix

Try it

Copy these to start.

Use these prompts to trigger the calculation immediately.

Starting points

Replace the bracketed text with your actual data or file paths.

Native V8 Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_lcKPfQMdEs9vOL0QvMD7tp5xcmCCFVUKYwiOuwlI/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Native V8 capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "confusion-matrix-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_lcKPfQMdEs9vOL0QvMD7tp5xcmCCFVUKYwiOuwlI/mcp"
    }
  }
}
Copy into chat04

  • Calculate the precision, recall, and F1-score for these labels: true_labels=[1, 0, 1, 1], pred_labels=[1, 1, 0, 1]

  • Use the Confusion Matrix Engine to analyze the performance of the model results in my 'eval_results.json' file.

  • I have two arrays of model predictions in this spreadsheet. Can you run a confusion matrix on them?

  • Compare the precision and recall for my classification model using the data from my current workspace.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Confusion Matrix Engine once, then ask.

Just click the link to connect. Your credentials stay encrypted, and you can start asking your AI for metrics immediately.

Connect Confusion Matrix Engine to your AI

FAQ

How the engine behaves

  • 01

    Is the math probabilistic or deterministic?

    It is deterministic. The engine uses fixed mathematical formulas to ensure the results are exact and repeatable.

  • 02

    Can the AI change my original data files?

    No. The AI only reads your label arrays to perform calculations. It does not modify your source files or datasets.

  • 03

    Can it calculate metrics for images?

    No. This specific tool is designed for label arrays and numerical prediction data, not for direct image or video analysis.

  • 04

    Does it work with any AI client?

    Yes, as long as your client is MCP-compatible, like Claude, Cursor, or Windsurf.

  • 05

    Will the AI decide if my model is 'good enough'?

    No. The AI provides the statistical metrics, but the final judgment on whether those numbers meet your standards is yours.

  • More questions about Confusion Matrix Engine? The Connector page answers them. See everything the Confusion Matrix Engine Connector can do

Connect Native V8 to Claude, Cursor, ChatGPT & more