• SCALE
  • PROPORTION
  • DATA
  • HUMAN

How to validate data visualizations with your AI

Connect this MCP to your AI client once. Your agent then audits charts to find mathematical or visual deceptions.

Ask AI about this page

Short answer

How can I use AI to check if my charts are misleading?

Your AI uses the Data Analysis Prover to scan visual data for truncated Y-axes, skewed scales, or mismatched proportions. It flags these errors so you can fix them before presenting. You'll get a breakdown of every visual lie it finds.

Where every issue lands.

The audit outcomes

The AI categorizes every discrepancy it finds into these four buckets.

SCALE

Axis integrity checks

The AI identifies truncated Y-axes or non-zero baselines that exaggerate trends. You get the exact mathematical reason why the scale is dishonest.

PROPORTION

Visual ratio errors

It catches 3D effects or area exaggerations that make small changes look massive. You'll see where the visual weight doesn't match the raw numbers.

DATA

Value mismatches

The agent compares the visual heights of bars or points against the underlying dataset. It flags any visual element that doesn't align with the actual values.

HUMAN

Final judgment

The AI highlights the most egregious deceptions for your review. You decide if the chart needs a total redesign or just a minor adjustment.

The workflow

What your AI does when the data arrives.

The AI doesn't just look at the image; it cross-references the visual with the math.

  1. Visual scanning

    Your agent looks at the chart to identify the axes, legends, and data points. It maps out the visual structure to prepare for the audit.

    validate_data_analysis

  2. Scale verification

    The AI checks if the Y-axis starts at zero or if the intervals are consistent. It looks for any tricks used to make growth look steeper than it is.

    validate_data_analysis

  3. Mathematical audit

    It runs a check between the visual representation and the source numbers. This catches cases where a bar is twice as tall as it should be.

    validate_data_analysis

  4. Error reporting

    The agent compiles a list of every misleading element it found. It presents these as actionable points for you to correct.

    validate_data_analysis

Try it

Copy these to start.

Paste these directly into your chat to begin the audit.

Starting points

Swap the bracketed text for your specific files or links. These are just ways to kick off the conversation.

Data Analysis Prover Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/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 — Data Analysis Prover capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "data-analysis-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_z0UOTGMw5z9e0sSzQndQJ5ZX9DZMH1doH3kzey3a/mcp"
    }
  }
}
Copy into chat04

  • Check this bar chart from my Google Sheet for any truncated Y-axes.

  • Does this line graph in my local CSV file use a misleading scale?

  • Audit the proportions in this screenshot from my project folder.

  • Look at the data in this Excel file and tell me if the pie chart is dishonest.

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

Start here

Connect Data Analysis Prover once, then ask.

Just click the link to connect the MCP to your client. Your credentials stay encrypted, and you can start auditing charts immediately.

Connect Data Analysis Prover to your AI

FAQ

How this task behaves

  • 01

    Can the AI fix the chart for me?

    No. The AI identifies the errors and tells you what is wrong, but you must make the actual visual changes in your charting tool.

  • 02

    Does the AI change my original data files?

    No. The AI only reads your data to perform the validation. It never writes to or modifies your source files.

  • 03

    Can it check charts that are just images?

    Yes, as long as your AI client can process the image and pass the visual context to the MCP.

  • 04

    Will it catch every single tiny error?

    It catches mathematical and structural deceptions. It won't catch subjective design choices like color preference or font choice.

  • 05

    Can the AI generate new charts?

    No. This MCP is built to validate existing visualizations, not to create new ones.

  • More questions about Data Analysis Prover? The Connector page answers them. See everything the Data Analysis Prover Connector can do

Connect Data Analysis Prover to Claude, Cursor, ChatGPT & more