Image Diff

Name: Image Diff
Author: pulsemcp

作者：pulsemcp版本 0.1.3i★ –i在你的电脑上运行

注册名称com.pulsemcp/image-diff

安装

概览

作者提供的说明（英文原文）

MCP server for programmatic image comparison and visual diff generation.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 设计、图片与音频

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

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

在终端中运行这条命令

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

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过域名 pulsemcp.com 验证（注册中心会核实域名所有权）
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“设计、图片与音频”中的其他服务器比较i

星标数多于此分类中其他 2169 个服务器的 0%
发布到注册中心的时间比此分类中其他 2169 个服务器的 13% 更新
基础检查结果等于或优于此分类中其他 2169 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Image Diff 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.1.3）于5个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它？

同一分类中有：Figma Context MCP, Claude Real Video, Figma MCP Server

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Figma Context MCP

作者：GLips

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

在你的电脑上运行★ 16,165MIT5个月前更新

安装

Claude Real Video

作者：HUANGCHIHHUNGLeo

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

在你的电脑上运行★ 2,284MIT2个月前更新

安装

Figma MCP Server

作者：figma

The Figma MCP server brings Figma design context directly into your AI workflow.

在云端运行★ 2,15213个月前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

只显示了开头部分；其余内容请在仓库中阅读