Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
作者提供的说明(英文原文)
On-device image tools: background removal, compression, resize, crop, AI upscale, metadata removal.
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。
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。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
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": {
"ruah": {
"command": "npx",
"args": [
"-y",
"ruah-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add ruah -- npx -y ruah-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"ruah": {
"command": "npx",
"args": [
"-y",
"ruah-mcp"
]
}
}
}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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"ruah": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"ruah-mcp"
]
}
}
}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。
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9日
注册名称已通过域名 ruah.tools 验证(注册中心会核实域名所有权)
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
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)。
GitHub 仓库最后更新于–,最新版本(0.1.1)于6天前进入注册中心。
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。
同一分类中有:Figma Context MCP, Claude Real Video, Figma MCP Server
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ 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
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
The Figma MCP server brings Figma design context directly into your AI workflow.
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 tools for AI agents, as an MCP server. Background removal, compression, resizing, cropping, AI upscaling and metadata removal all run on your own computer. Images never leave it.
It is the engine of ruah.tools: the same models, encoders and settings, packaged for an agent to call.
| Tool | What it does |
|---|---|
remove_background | Cuts the main subject out of a photo and saves a transparent PNG (AI). |
compress | Makes an image file smaller with little visible loss, or converts it: WebP, AVIF, JPEG, PNG. |
resize | Changes the pixel dimensions, keeping proportions (by longest side or percent). |
crop | Crops to an aspect ratio or exact region, rotates by quarter turns, flips. Pixels are moved, never resampled. |
upscale | Enlarges 2× or 4× with AI super-resolution. Keeps transparency. |
strip_metadata | Removes EXIF (GPS location, camera, date), XMP, IPTC, comments and embedded thumbnails. JPEG and PNG are cleaned without re-encoding. |
Files in, files out: every tool writes a new file next to the original (or to the path you give) and returns its path with a small preview. Existing files, the original included, are never overwritten. Images up to 12000×12000 pixels (144 MP) and 512 MB are accepted; upscale results are capped at 4096×4096.
Requires Node.js 20 or newer.
Claude Code
claude mcp add ruah -- npx -y ruah-mcp
Or as a plugin, which adds a short skill on chaining the tools:
claude plugin marketplace add 0-big/ruah-mcp
claude plugin install ruah@ruah-tools
Any other MCP client (Claude Desktop, Cursor, …): add this to its MCP server config.
{
"mcpServers": { "ruah": { "command": "npx", "args": ["-y", "ruah-mcp"] } } }
Then ask your agent things like *"remove the background from ~/Desktop/shoe.jpg"* or *"convert these screenshots to WebP"*.
Only the AI model weights, once, from Ruah's model mirror (models.ruah.tools). Each file is checked against a SHA-256 hash pinned in this package before it is used, then kept in a local cache:
| Model | Used by | Size | When |
|---|---|---|---|
| BiRefNet lite (512²) | remove_background | 98 MB | Fetched in the background when the server first starts |
| BiRefNet (512²) | remove_background | 473 MB | Only if the lite model finds no subject |
| Swin2SR ×2 | upscale 2× | 8 MB | First 2× upscale |
| Swin2SR ×4 | upscale 4× | 55 MB | First 4× upscale |
Compression, resizing, cropping and metadata removal need no download at all.
Installing is separate: npx fetches the package and its dependencies from npm, and on Linux x64 the install script of onnxruntime-node (the AI runtime) also fetches about 260 MB of CUDA and TensorRT files from NuGet, which Ruah never uses. Set ONNXRUNTIME_NODE_INSTALL=skip in the server's environment to leave them out (the Claude Code plugin does).
The cache lives in ~/.cache/ruah/models (or $XDG_CACHE_HOME/ruah/models). Set RUAH_CACHE_DIR to put it elsewhere.
ruah-mcp is MIT licensed (see LICENSE). The models it downloads keep their own licenses: BiRefNet (MIT) and Swin2SR (Apache-2.0). Lossy PNG compression uses libimagequant 2.4.1, its last BSD-licensed release, compiled to WebAssembly and bundled in dist/index.js. Its notice is dist/LICENSE-libimagequant.txt.