Image Diff

Name: Image Diff
Author: pulsemcp

by pulsemcpversion 0.1.3i★ –iRuns on your computer

Registry namecom.pulsemcp/image-diff

Install

Overview

Description from the maker (original English)

MCP server for programmatic image comparison and visual diff generation.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "image-diff": {
      "command": "npx",
      "args": [
        "-y",
        "@pulsemcp/image-diff-mcp-server"
      ]
    }
  }
}

Run this in your terminal

claude mcp add image-diff -- npx -y @pulsemcp/image-diff-mcp-server

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "image-diff": {
      "command": "npx",
      "args": [
        "-y",
        "@pulsemcp/image-diff-mcp-server"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "image-diff": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@pulsemcp/image-diff-mcp-server"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain pulsemcp.com (the registry checks domain ownership)
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2170 servers in this category
Published to the registry more recently than 13% of the other 2170 servers in this category
Basic checks equal to or better than 100% of the other 2170 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Image Diff safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.3) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Figma Context MCP

by GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

Runs on your computer★ 16,165MITUpdated 5 months ago

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Claude Real Video

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Let any LLM watch a video locally — and search everything it has ever watched.

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Figma MCP Server

by figma

Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

image-diff MCP Server

A Model Context Protocol (MCP) server for programmatic image comparison and visual diff generation. Compares two images pixel-by-pixel, identifies clusters of visual differences, and generates heatmap visualizations.

Designed for comparing design mock screenshots against actual UI implementations.

How It Works

Pixel comparison using YIQ NTSC perceptual color distance (accounts for how humans perceive color differences)
Anti-aliasing detection to filter out false positives from font smoothing and rounded corners
Connected Component Labeling to group adjacent diff pixels into spatial clusters
Natural-breaks clustering to auto-compute an optimal cluster_gap using nearest-neighbor distance analysis, merging nearby clusters into meaningful groups
Heatmap generation with yellow-to-red intensity gradient showing severity of differences
Automatic alignment for different-sized images using OpenCV ZNCC template matching

Tools

`get_diff_of_images`

Compare two images and get structured diff results.

Parameters:

Parameter	Type	Required	Default	Description
`source_image`	string	Yes	-	Absolute path to the reference image
`target_image`	string	Yes	-	Absolute path to the comparison image
`threshold`	number	No	0.1	Color matching sensitivity (0-1, lower = more sensitive)
`include_aa`	boolean	No	false	Count anti-aliased pixels as differences
`min_cluster_size`	number	No	4	Minimum connected pixels to form a cluster
`cluster_gap`	number	No	auto	Max pixel gap to merge nearby clusters. When omitted, an optimal gap is auto-computed using nearest-neighbor distance analysis with natural breaks. Set to 0 for no merging, or any positive integer for manual control.

Returns:

identical: boolean indicating if images match
summary: overall diff statistics (pixel counts, percentages, cluster count)
clusters: array of diff regions with bounding boxes, severity ratings, and intensity metrics
clustering: gap used, whether it was auto-computed, and suggested alternative gap values
alignment (when images differ in size): position coordinates, confidence score, strategy used, and timing
dimensions: width and height of the compared region
heatmapPath: file path to the generated heatmap image
compositePath: file path to the heatmap overlaid on the source image

Requirements:

Images must be accessible as local file paths
Supports PNG, JPEG, WebP, TIFF input formats
Images can have different dimensions — the smaller image is automatically aligned within the larger one

Tool Call Shape

MCP tool call input (minimal — just the two required paths):

"name": "get_diff_of_images", "arguments": { "source_image": "/path/to/design-mock.png", "target_image": "/path/to/screenshot.png" } }

MCP tool call input (with all optional parameters):

"name": "get_diff_of_images", "arguments": { "source_image": "/path/to/design-mock.png", "target_image": "/path/to/screenshot.png", "threshold": 0.05, "include_aa": true, "min_cluster_size": 10, "cluster_gap": 15 } }

MCP response (same-size images with differences):

"content": [ { "type": "text", "text": "{\n \"identical\": false,\n \"summary\": {\n \"totalPixels\": 480000,\n \"diffPixels\": 737,\n \"diffPercentage\": 0.154,\n \"antiAliasedPixels\": 1034,\n \"matchingPixels\": 478229,\n \"clusterCount\": 11,\n \"description\": \"0.15% of pixels differ across 11 cluster(s). Severity breakdown: 5 major, 5 minor, 1 trivial.\"\n },\n \"clustering\": {\n \"gapUsed\": 4,\n \"autoGap\": true,\n \"suggestedSmallerGap\": 3,\n \"suggestedLargerGap\": 5\n },\n \"clusters\": [ ... ],\n \"heatmapPath\": \"/tmp/image-diff-output/heatmap-1234567890.png\",\n \"compositePath\": \"/tmp/image-diff-output/composite-1234567890.png\",\n \"dimensions\": { \"width\": 800, \"height\": 600 }\n}" } ] }

The text field contains the full JSON result as a string. When parsed, the full response shape looks like this:

Full parsed response shape (same-size images, with differences)

"identical": false, "summary": { "totalPixels": 480000, "diffPixels": 737, "diffPercentage": 0.154, "antiAliasedPixels": 1034, "matchingPixels": 478229, "clusterCount": 11, "description": "0.15% of pixels differ across 11 cluster(s). Severity breakdown: 5 major, 5 minor, 1 trivial." }, "clustering": { "gapUsed": 4, "autoGap": true, "suggestedSmallerGap": 3, "suggestedLargerGap": 5 }, "clusters": [ { "id": 1, "left": 444, "top": 157, "right": 504, "bottom": 165, "width": 61, "height": 9, "pixelCount": 170, "totalImagePixels": 480000, "areaPercentage": 0.035, "meanIntensity": 0.111, "maxIntensity": 0.343, "severity": "minor" } ], "heatmapPath": "/tmp/image-diff-output/heatmap-1234567890.png", "compositePath": "/tmp/image-diff-output/composite-1234567890.png", "dimensions": { "width": 800, "height": 600 } }

Full parsed response shape (identical images)

"identical": true, "summary": { "totalPixels": 480000, "diffPixels": 0, "diffPercentage": 0, "antiAliasedPixels": 0, "matchingPixels": 480000, "clusterCount": 0, "description": "Images are identical (no differences detected)." }, "clustering": { "gapUsed": 0, "autoGap": true, "suggestedSmallerGap": null, "suggestedLargerGap": null }, "clusters": [], "heatmapPath": "/tmp/image-diff-output/heatmap-1234567890.png", "compositePath": "/tmp/image-diff-output/composite-1234567890.png", "dimensions": { "width": 800, "height": 600 } }

Full parsed response shape (different-size images — auto-alignment)

"identical": false, "summary": { "totalPixels": 108800, "diffPixels": 9031, "diffPercentage": 8.301, "antiAliasedPixels": 329, "matchingPixels": 99440, "clusterCount": 7, "description": "8.30% of pixels differ across 7 cluster(s). Severity breakdown: 1 major, 3 moderate, 3 minor." }, "clustering": { "gapUsed": 4, "autoGap": true, "suggestedSmallerGap": 1, "suggestedLargerGap": 6 }, "clusters": [ { "id": 1, "left": 20, "top": 185, "right": 299, "bottom": 215, "width": 280, "height": 31, "pixelCount": 8372, "totalImagePixels": 108800, "areaPercentage": 7.695, "meanIntensity": 0.242, "maxIntensity": 0.244, "severity": "major" } ], "alignment": { "x": 680, "y": 212, "confidence": 0.993, "strategy": "opencv-zncc-hybrid", "alignmentTimeMs": 89, "templateImage": "target", "originalDimensions": { "source": { "width": 1024, "height": 768 }, "target": { "width": 320, "height": 340 } } }, "heatmapPath": "/tmp/image-diff-output/heatmap-1234567890.png", "compositePath": "/tmp/image-diff-output/composite-1234567890.png", "dimensions": { "width": 320, "height": 340 } }

Note: The alignment field only appears when images have different dimensions. The dimensions field reflects the size of the compared region (the template size after alignment, not the full scene). The compositePath field is optional in the interface but always present when called via the MCP tool.

Examples

All examples below use this dashboard as the source image (800x600):

Example 1: Identical Images — No Differences

When two images are pixel-identical, the engine short-circuits and returns immediately.

Source	Target	Composite

The composite overlay shows the source image with no heatmap highlights — because there are no differences.

Structured output

Example 2: Color & Value Changes

The target has updated stat values ("12,847" -> "13,205", "$48,290" -> "$52,180") and different percentage text. The engine finds 11 clusters (auto gap=4) across the changed text and numbers.

Source	Target

Standalone Heatmap	Composite Overlay

The standalone heatmap shows only the diff pixels on a transparent background — yellow means lower intensity, red means higher intensity. The composite overlay blends the heatmap onto the source image so you can see exactly where differences occur in context.

Structured output (11 clusters, top 3 shown)

"identical": false, "summary": { "totalPixels": 480000, "diffPixels": 737, "diffPercentage": 0.154, "antiAliasedPixels": 1034, "matchingPixels": 478229, "clusterCount": 11, "description": "0.15% of pixels differ across 11 cluster(s). Severity breakdown: 5 major, 5 minor, 1 trivial." }, "clustering": { "gapUsed": 4, "autoGap": true, "suggestedSmallerGap": 3, "suggestedLargerGap": 5 }, "clusters": [ { "id": 1, "left": 444, "top": 157, "right": 504, "bottom": 165, "width": 61, "height": 9, "pixelCount": 170, "areaPercentage": 0.035, "meanIntensity": 0.111, "maxIntensity": 0.343, "severity": "minor" }, { "id": 2, "left": 450, "top": 125, "right": 478, "bottom": 142, "width": 29, "height": 18, "pixelCount": 112, "areaPercentage": 0.023, "meanIntensity": 0.677, "maxIntensity": 0.738, "severity": "major" }, { "id": 3, "left": 284, "top": 126, "right": 308, "bottom": 142, "width": 25, "height": 17, "pixelCount": 91, "areaPercentage": 0.019, "meanIntensity": 0.682, "maxIntensity": 0.738, "severity": "major" } ] }

_8 more clusters omitted._

Example 3: Font Change (sans-serif -> serif)

The entire page is re-rendered with a serif font (Georgia). Every text element shifts slightly, producing many glyph-level differences. With the default auto-gap, the engine computes an optimal `c

Only the beginning is shown; read the rest in the repo

Image Diff

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

image-diff MCP Server

How It Works

Tools

get_diff_of_images

Tool Call Shape

Examples

Example 1: Identical Images — No Differences

Example 2: Color & Value Changes

Example 3: Font Change (sans-serif -> serif)

`get_diff_of_images`