mcpField
I
image-diff

image-diff

Local
github.com/pulsemcpDeveloper Tools81Updated 8w ago
Repository

About image-diff

image-diff is an MCP server for comparing local images, aimed at checking design mockups against UI screenshots. It groups visual changes and produces heatmap outputs, with automatic alignment for differently sized images.

Key features

  1. 01Pixel-level color comparison
  2. 02Anti-aliasing filtering
  3. 03Grouped difference regions
  4. 04Automatic cluster-gap calculation
  5. 05Heatmap and composite generation
  6. 06Alignment of different-sized images

Use cases

  1. 01Compare design mockups with UI screenshots
  2. 02Locate changed text, colors, or layout
  3. 03Compare a component image with a full-page screenshot

MCP support

It exposes the `get_diff_of_images` tool to MCP clients. The tool accepts local image paths and returns difference statistics, clustered regions, and heatmap file paths.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "com-pulsemcp-image-diff": {
      "command": "npx",
      "args": [
        "-y",
        "@pulsemcp/image-diff-mcp-server"
      ]
    }
  }
}
Run commandnpx -y @pulsemcp/image-diff-mcp-server

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@pulsemcp/image-diff-mcp-server0.1.3

Tools

1

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
thresholdnumberNoColor matching sensitivity from 0 to 1. Lower values detect subtler differences. 0.1 is a good default for UI comparison. Use 0.05 for strict comparison or 0.2 for lenient.
include_aabooleanNoWhether to count anti-aliased pixels (font smoothing, rounded corners) as differences. Default false excludes them to reduce noise.
cluster_gapintegerNoMaximum pixel distance between cluster bounding boxes to merge them into a single cluster. When omitted, an optimal gap is auto-computed using natural breaks in cluster distances. Set to 0 to disable merging entirely, or 5-20 for manual control.
source_imagestringYesAbsolute file path to the source/reference image (e.g. the design mock). Supports PNG, JPEG, WebP, TIFF.
target_imagestringYesAbsolute file path to the target/comparison image (e.g. the actual UI screenshot). Supports PNG, JPEG, WebP, TIFF.
min_cluster_sizeintegerNoMinimum number of connected diff pixels to report as a cluster. Filters out isolated pixel noise. Default 4.

Details

From the project README

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

  1. Pixel comparison using YIQ NTSC perceptual color distance (accounts for how humans perceive color differences)
  2. Anti-aliasing detection to filter out false positives from font smoothing and rounded corners
  3. Connected Component Labeling to group adjacent diff pixels into spatial clusters
  4. Natural-breaks clustering to auto-compute an optimal cluster_gap using nearest-neighbor distance analysis, merging nearby clusters into meaningful groups
  5. Heatmap generation with yellow-to-red intensity gradient showing severity of differences
  6. 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:

ParameterTypeRequiredDefaultDescription
source_imagestringYes-Absolute path to the reference image
target_imagestringYes-Absolute path to the comparison image
thresholdnumberNo0.1Color matching sensitivity (0-1, lower = more sensitive)
include_aabooleanNofalseCount anti-aliased pixels as differences
min_cluster_sizenumberNo4Minimum connected pixels to form a cluster
cluster_gapnumberNoautoMax 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):

Source dashboard

Example 1: Identical Images — No Differences

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

SourceTargetComposite
sourcetargetcomposite

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

Structured output 
{
  "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
  }
}
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.

SourceTarget
sourcetarget
Standalone HeatmapComposite Overlay
heatmapcomposite

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 cluster_gap=10 and merges these into 40 clusters representing distinct text blocks and UI regions.

SourceTarget
sourcetarget
Standalone HeatmapComposite Overlay
heatmapcomposite

The heatmap lights up across every text region — headers, stat values, table content, sidebar labels. Auto-gap merges nearby glyph fragments into meaningful groups.

Structured output — default auto-gap (40 clusters, top 3 shown) 
{
  "identical": false,
  "summary": {
    "totalPixels": 480000,
    "diffPixels": 11671,
    "diffPercentage": 2.431,
    "antiAliasedPixels": 8326,
    "matchingPixels": 460003,
    "clusterCount": 40,
    "description": "2.43% of pixels differ across 40 cluster(s). Severity breakdown: 26 moderate, 14 minor."
  },
  "clustering": {
    "gapUsed": 10,
    "autoGap": true,
    "suggestedSmallerGap": 9,
    "suggestedLargerGap": 11
  },
  "clusters": [
    {
      "id": 1,
      "left": 434,
      "top": 125,
      "right": 550,
      "bottom": 166,
      "width": 117,
      "height": 42,
      "pixelCount": 912,
      "severity": "moderate"
    },
    {
      "id": 2,
      "left": 246,
      "top": 125,
      "right": 372,
      "bottom": 166,
      "width": 127,
      "height": 42,
      "pixelCount": 883,
      "severity": "moderate"
    },
    {
      "id": 3,
      "left": 625,
      "top": 125,
      "right": 733,
      "bottom": 166,
      "width": 109,
      "height": 42,
      "pixelCount": 716,
      "severity": "moderate"
    }
  ]
}

37 more clusters omitted. Note: same diff pixels, same percentage — only the grouping changes.

What Auto-Gap Saves You From: cluster_gap=0

Without auto-gap, each glyph fragment becomes its own cluster. Setting cluster_gap=0 explicitly disables merging:

cluster_gapClustersWhat Happens
auto (default)40Auto-computed gap=10 merges fragments into text blocks
0 (no merging)331Every glyph fragment is a separate cluster

With cluster_gap=0, the 40 meaningful groups explode into 331 raw clusters — one for every individual character difference. This is what auto-gap saves you from.

Structured output with cluster_gap=0 (331 clusters, top 3 shown) 
{
  "identical": false,
  "summary": {
    "totalPixels": 480000,
    "diffPixels": 11671,
    "diffPercentage": 2.431,
    "antiAliasedPixels": 8326,
    "matchingPixels": 460003,
    "clusterCount": 331,
    "description": "2.43% of pixels differ across 331 cluster(s). Severity breakdown: 61 major, 161 moderate, 105 minor, 4 trivial."
  },
  "clustering": {
    "gapUsed": 0,
    "autoGap": false,
    "suggestedSmallerGap": null,
    "suggestedLargerGap": 1
  },
  "clusters": [
    {
      "id": 1,
      "left": 65,
      "top": 88,
      "right": 110,
      "bottom": 98,
      "width": 46,
      "height": 11,
      "pixelCount": 221,
      "severity": "moderate"
    },
    {
      "id": 2,
      "left": 555,
      "top": 317,
      "right": 597,
      "bottom": 329,
      "width": 43,
      "height": 13,
      "pixelCount": 213,
      "severity": "moderate"
    },
    {
      "id": 3,
      "left": 555,
      "top": 391,
      "right": 597,
      "bottom": 403,
      "width": 43,
      "height": 13,
      "pixelCount": 213,
      "severity": "moderate"
    }
  ]
}

328 more clusters omitted. Note: same diff pixels, same percentage — only the grouping changes.

Example 4: Missing Elements (Badges Removed)

The target has status badges ("Complete", "Active") removed from the table — replaced with plain text. The engine detects 19 clusters (auto gap=4) precisely locating each removed badge.

SourceTarget
sourcetarget
Standalone HeatmapComposite Overlay
heatmapcomposite

The heatmap highlights each badge position where the colored pill was replaced with plain text.

Structured output (19 clusters, top 3 shown) 
{
  "identical": false,
  "summary": {
    "totalPixels": 480000,
    "diffPixels": 4506,
    "diffPercentage": 0.939,
    "antiAliasedPixels": 2283,
    "matchingPixels": 473211,
    "clusterCount": 19,
    "description": "0.94% of pixels differ across 19 cluster(s). Severity breakdown: 18 moderate, 1 minor."
  },
  "clustering": {
    "gapUsed": 4,
    "autoGap": true,
    "suggestedSmallerGap": 3,
    "suggestedLargerGap": 5
  },
  "clusters": [
    {
      "id": 1,
      "left": 369,
      "top": 426,
      "right": 487,
      "bottom": 439,
      "width": 119,
      "height": 14,
      "pixelCount": 516,
      "areaPercentage": 0.108,
      "severity": "moderate"
    },
    {
      "id": 2,
      "left": 369,
      "top": 352,
      "right": 462,
      "bottom": 365,
      "width": 94,
      "height": 14,
      "pixelCount": 442,
      "areaPercentage": 0.092,
      "severity": "moderate"
    },
    {
      "id": 3,
      "left": 657,
      "top": 427,
      "right": 723,
      "bottom": 439,
      "width": 67,
      "height": 13,
      "pixelCount": 311,
      "areaPercentage": 0.065,
      "severity": "moderate"
    }
  ]
}

16 more clusters omitted.

Example 5: Layout Shift (Padding Change)

The target has increased padding and gaps on the stat cards — everything shifts down slightly. This produces 18 clusters (auto gap=18) because the auto-gap merges the many edge-level fragments caused by content repositioning.

SourceTarget
sourcetarget
Standalone HeatmapComposite Overlay
heatmapcomposite

Layout shifts light up the heatmap everywhere content has repositioned. The auto-gap merges the ripple effect into cohesive regions representing each shifted element.

Structured output (18 clusters, top 3 shown) 
{
  "identical": false,
  "summary": {
    "totalPixels": 480000,
    "diffPixels": 11136,
    "diffPercentage": 2.32,
    "antiAliasedPixels": 10938,
    "matchingPixels": 457926,
    "clusterCount": 18,
    "description": "2.32% of pixels differ across 18 cluster(s). Severity breakdown: 18 moderate."
  },
  "clustering": {
    "gapUsed": 18,
    "autoGap": true,
    "suggestedSmallerGap": 17,
    "suggestedLargerGap": 19
  },
  "clusters": [
    {
      "id": 1,
      "left": 434,
      "top": 105,
      "right": 550,
      "bottom": 195,
      "width": 117,
      "height": 91,
      "pixelCount": 1531,
      "areaPercentage": 0.319,
      "severity": "moderate"
    },
    {
      "id": 2,
      "left": 369,
      "top": 315,
      "right": 490,
      "bottom": 448,
      "width": 122,
      "height": 134,
      "pixelCount": 1490,
      "areaPercentage": 0.31,
      "severity": "moderate"
    },
    {
      "id": 3,
      "left": 245,
      "top": 103,
      "right": 372,
      "bottom": 195,
      "width": 128,
      "height": 93,
      "pixelCount": 1473,
      "areaPercentage": 0.307,
      "severity": "moderate"
    }
  ]
}

15 more clusters omitted.

Auto-Alignment: Comparing Different-Sized Images

When the source and target have different dimensions, the engine automatically finds where the smaller image (the "template") best matches within the larger one (the "scene"), crops that region, and runs the standard pixel-level comparison. No configuration needed.

How it works:

  1. Coarse search — OpenCV ZNCC (Zero-mean Normalized Cross-Correlation) template matching at downscaled resolution for fast candidate detection
  2. Fine refinement — Pixel-level ZNCC at full resolution around the coarse match for sub-pixel accuracy
  3. Crop & compare — The matched region is cropped from the scene and compared pixel-by-pixel against the template

This enables comparing a Figma component mock against a full-page screenshot directly — no manual cropping needed.

All alignment examples below use this dashboard as the source (scene) image (1024x768):

Full page dashboard

Auto-Align Example 1: Stats Card Mock vs Full Page

A Figma mock of the stats row (780x100) compared against the full page (1024x768). The mock has updated numbers ("12,847" -> "14,205", "$48,290" -> "$52,180", "3,847" -> "4,102") and different percentages.

Full Page (1024x768)Stats Card Mock (780x100)
fullmock

The engine automatically locates the stats row at position (238, 80), crops that 780x100 region from the full page, and compares it against the mock.

Composite Overlay (on cropped region)
composite

The heatmap highlights exactly the changed stat values and percentages within the aligned region.

Structured output (15 clusters, top 3 shown) 
{
  "identical": false,
  "summary": {
    "totalPixels": 78000,
    "diffPixels": 2570,
    "diffPercentage": 3.295,
    "antiAliasedPixels": 2503,
    "matchingPixels": 72927,
    "clusterCount": 15,
    "description": "3.29% of pixels differ across 15 cluster(s). Severity breakdown: 5 major, 4 moderate, 6 minor."
  },
  "clustering": {
    "gapUsed": 5,
    "autoGap": true,
    "suggestedSmallerGap": 4,
    "suggestedLargerGap": 6
  },
  "alignment": {
    "x": 238,
    "y": 80,
    "confidence": 0.743,
    "strategy": "opencv-zncc-hybrid",
    "alignmentTimeMs": 146,
    "templateImage": "target",
    "originalDimensions": {
      "source": { "width": 1024, "height": 768 },
      "target": { "width": 780, "height": 100 }
    }
  },
  "clusters": [
    {
      "id": 1,
      "left": 607,
      "top": 45,
      "right": 679,
      "bottom": 66,
      "width": 73,
      "height": 22,
      "pixelCount": 421,
      "areaPercentage": 0.54,
      "severity": "major"
    },
    {
      "id": 2,
      "left": 413,
      "top": 45,
      "right": 480,
      "bottom": 66,
      "width": 68,
      "height": 22,
      "pixelCount": 412,
      "areaPercentage": 0.528,
      "severity": "major"
    },
    {
      "id": 3,
      "left": 22,
      "top": 45,
      "right": 103,
      "bottom": 66,
      "width": 82,
      "height": 22,
      "pixelCount": 369,
      "areaPercentage": 0.473,
      "severity": "major"
    }
  ]
}

12 more clusters omitted.

Key fields in the alignment object:

  • x, y — where the template was found in the scene (pixel coordinates)
  • confidence — match quality (0-1, higher is better)
  • strategy — algorithm used (opencv-zncc-hybrid)
  • alignmentTimeMs — how long alignment took
  • templateImage — which image was the template ("target" means the target was smaller)
  • originalDimensions — original sizes before alignment
Auto-Align Example 2: Sidebar Panel Mock vs Full Page

A Figma mock of the right sidebar (320x340) with modified progress values (78->85%, 92->95%, 45->62%) and a different button color (blue -> red). Compared against the full page.

Full Page (1024x768)Sidebar Mock (320x340)
fullmock

The engine finds the sidebar at position (680, 212) with 99.3% confidence in 89ms, then identifies the changed progress bars and button.

Composite Overlay (on cropped region)Standalone Heatmap
compositeheatmap

The major cluster at the bottom is the button color change (blue -> red). The moderate clusters are the progress bar fill changes.

Structured output (7 clusters, top 3 shown) 
{
  "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
  },
  "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 }
    }
  },
  "clusters": [
    {
      "id": 1,
      "left": 20,
      "top": 185,
      "right": 299,
      "bottom": 215,
      "width": 280,
      "height": 31,
      "pixelCount": 8372,
      "areaPercentage": 7.695,
      "severity": "major"
    },
    {
      "id": 2,
      "left": 146,
      "top": 157,
      "right": 192,
      "bottom": 164,
      "width": 47,
      "height": 8,
      "pixelCount": 370,
      "areaPercentage": 0.34,
      "severity": "moderate"
    },
    {
      "id": 3,
      "left": 238,
      "top": 71,
      "right": 256,
      "bottom": 78,
      "width": 19,
      "height": 8,
      "pixelCount": 146,
      "areaPercentage": 0.134,
      "severity": "moderate"
    }
  ]
}

4 more clusters omitted.

Auto-Align Example 3: Identical Crop — 0% Diff

An exact crop of the activity table (420x496) from the same full page screenshot. The engine finds the table at the exact position and confirms 0% diff — proving alignment precision.

Full Page (1024x768)Table Crop (420x496)
fullmock
Composite Overlay (clean — no diffs)
composite
Structured output 
{
  "identical": true,
  "summary": {
    "totalPixels": 208320,
    "diffPixels": 0,
    "diffPercentage": 0,
    "antiAliasedPixels": 0,
    "matchingPixels": 208320,
    "clusterCount": 0,
    "description": "Images are identical (no differences detected)."
  },
  "clustering": {
    "gapUsed": 0,
    "autoGap": true,
    "suggestedSmallerGap": null,
    "suggestedLargerGap": null
  },
  "alignment": {
    "x": 244,
    "y": 212,
    "confidence": 1,
    "strategy": "opencv-zncc-hybrid",
    "alignmentTimeMs": 99,
    "templateImage": "target",
    "originalDimensions": {
      "source": { "width": 1024, "height": 768 },
      "target": { "width": 420, "height": 496 }
    }
  }
}

100% confidence, 0 diff pixels. The alignment found the exact position and the cropped region matches perfectly.

Alignment Performance
ScenarioTemplateScenePosition FoundConfidenceTime
Stats card780x1001024x768(238, 80)74.3%146ms
Sidebar panel320x3401024x768(680, 212)99.3%89ms
Table (identical)420x4961024x768(244, 212)100%99ms

Cluster Severity Ratings

Each diff cluster is classified by severity based on pixel count and intensity:

SeverityMeaningTypical Cause
trivialIsolated pixel differencesSub-pixel rendering, compression artifacts
minorSmall localized differencesSlight color shifts, font weight changes
moderateNoticeable regional differencesText changes, badge modifications
majorLarge or intense differencesButton color changes, missing/added elements, layout shifts

Example Agent Skills

The example-skills/ directory contains ready-to-use Agent Skills that demonstrate how to build workflows on top of the image-diff MCP server.

implement-figma-design

An iterative design implementation skill that coordinates three MCP servers — Figma, Playwright, and image-diff — to build a UI component that matches a Figma design. The agent writes code, screenshots the running dev server, diffs against the Figma reference, diagnoses each diff cluster's root cause (color mismatch, typography, spacing, missing elements, etc.), applies fixes, and loops until the diff converges to <= 0.01%.

Pre-requisites: Figma MCP server, Playwright MCP server, image-diff MCP server, a working dev server.

To use: Copy example-skills/implement-figma-design/ into your project's .claude/skills/ directory (or equivalent skill location for your agent platform).

Credits

  • Pixel comparison algorithm forked from pixelmatch by Mapbox (ISC License)
  • Color distance based on "Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications" by Y. Kotsarenko and F. Ramos
  • Anti-aliasing detection based on "Anti-aliased Pixel and Intensity Slope Detector" by V. Vysniauskas, 2009
  • Connected Component Labeling uses the classic two-pass algorithm with Union-Find
  • Image alignment powered by opencv-wasm (Apache 2.0 License)
  • Image I/O powered by sharp (Apache 2.0 License)
  • Clustering approach inspired by looks-same by Gemini Testing (MIT License)

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