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)
Catches palette colors, native elements and APIs your design system lacks in agent-written React.
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": {
"onsystem": {
"command": "npx",
"args": [
"-y",
"onsystem"
]
}
}
}在终端中运行这条命令
claude mcp add onsystem -- npx -y onsystem粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"onsystem": {
"command": "npx",
"args": [
"-y",
"onsystem"
]
}
}
}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": {
"onsystem": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"onsystem"
]
}
}
}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6日
注册名称已通过 GitHub 账户 dgesteves 验证,该账户拥有此仓库
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(0.12.1)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:★ 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。
Keeps coding agents on your design system: catches palette colors, native elements and APIs your system lacks, at every edit and pull request, with the fix. Set it up once in your design-system package; every app in your monorepo gets it. Runs locally and works alongside @shadcn/lint.
Built for design-system and platform teams with a React design-system package; a shadcn/ui app is the zero-config demo. Formerly @dgesteves/design-system-mcp (what changed).
Using the GitHub Action? uses: dgesteves/onsystem@v0 checks the lines a pull request changed: see CI and baselines.
Also works with
Website and docs: onsystem.vercel.app, with a playground that checks your own code.
Feedback wanted: say where it breaks in the launch feedback thread, see what's next in the roadmap, or pick up a good first issue. If onsystem is useful to you, a star helps other people find it.
https://github.com/user-attachments/assets/900cc1fa-387c-4e76-a524-a818521dd1a0
On the website , an agent drafts a settings card, check_ui finds 11 problems with a fix each, and the agent applies them. Then a real Claude Code session with the plugin: Claude looks the components up before writing, and check_ui passes. Last, the benchmark.
Coding agents write UI from their training data, not from your design system. Ask for a settings card and you get bg-[#ef4444], p-[13px], a native , variant="danger" on a Button that only has destructive, and where the system exports CardHeader. On the demo draft above, tsc reports 3 of the 11 problems, and review catches the rest when someone has the time. Describing the design system to the agent helps; what holds is checking what it actually wrote, at the edit and at the pull request.
onsystem reads your components, cva and tv variants, parts and tokens from source with the TypeScript compiler, and checks .tsx and .jsx against them. Every finding has a rule id, a location and the fix. Tokens come from your stylesheets, DTCG 2025.10 files or a DESIGN.md, and onsystem export writes them out as either.
| Rule | Default | Catches |
|---|---|---|
no-unknown-component | error | Components and parts that don't exist ( , typos) |
no-unknown-prop | error | Props a component doesn't take (tone, isDisabled) |
no-unknown-variant | error | Values outside a variant (variant="danger") |
prefer-design-system-component | error | Native elements the design system has a component for ( → ) |
no-hardcoded-color | error | Hex, rgb, hsl, oklch and Tailwind palette colors (bg-gray-100 → bg-muted) |
icon-button-accessible-name | error | Icon-only buttons without an accessible name |
no-hardcoded-spacing | warn | Arbitrary padding, margin and gap (p-[13px] → p-3) |
no-hardcoded-radius | warn | Arbitrary radius (rounded-[7px] → rounded-sm) |
How often it is wrong. The corpus job runs check on 12 public repositories pinned by commit (Documenso, Dub, Cal.com, midday, Flowise and others: 24 runs, 14,539 distinct findings), without their dependencies installed (the two MUI apps get MUI). A seeded random sample of 80 of those findings, stratified by rule, was labelled from the code alone: blind labels by an AI reviewer (Claude Opus 5.5), not by a person. 57 are right, 14 debatable and 9 false positives: 11.2% (95% Wilson interval 6.0-20.0%), about the same when weighted by each rule's share of all findings (11.0%). Most come from no-hardcoded-color (7 of 65 sampled). Counting values a project's own design system already uses as debatable, 6 of 80 are false positives (7.5%, 3.5-15.4%). The sample has each label with its reason, the labeller's notes and the script behind these numbers. Shipped code rarely invents components, props or variants, and without dependencies no-unknown-prop skips components whose props do not resolve: those three rules have 56 findings in the corpus, 55 of them in Flowise, an MUI app, and 3 in the sample, all right but too few for a rate of their own. The corpus's older labels, made by an AI reviewer who could see the finding and so not blind, fail the job when one of their true positives stops being reported or a fixed false positive comes back. Code installed from a shadcn registry, such as the chatbot's components/ai-elements, has no labels yet (Code from shadcn registries). The rules reference shows each rule on an example and how to suppress a finding.
Run it from the app's folder or from the monorepo root, with Node.js 20.19 or later:
npx -y onsystem inspect # what it found; --explain says why
npx -y onsystem check . # the same check the agent and CI run npx -y onsystem init # write the config, and offer a baseline
At a monorepo root, inspect lists every app and design-system package, and check checks each file by its own project's design system. In a design-system package, init sets it up for every app that depends on it, with no config in the apps: as a workspace package, or installed from npm. Installed apps read the components from the sources the package publishes, or from its type declarations (.d.ts) when it publishes only compiled dist/, and get every rule either way (init checks its files list). From a package that publishes compiled code and no type declarations, they get the token rules but no component, prop, variant or native-element checks.
In a new create-next-app, the template page's hardcoded colors already make the first check . fail; init offers to record them as a baseline, so only new findings fail. Folders installed from a shadcn registry, such as components/ai-elements, are someone else's code: Code from shadcn registries says how to check them or leave them out.
Claude Code. The plugin brings the hook, the MCP server and a skill:
/plugin marketplace add dgesteves/onsystem
/plugin install onsystem@dgesteves
After each Write or Edit of a .tsx or .jsx file, the hook runs onsystem check on it. Errors go back to Claude as a blocking error with the fix, so Claude corrects the file before it moves on; warnings go as a note. It stays
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