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)
Validate design tokens for accessibility, scales, and design-system constraint consistency.
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": {
"design-constraint-validator": {
"command": "npx",
"args": [
"-y",
"design-constraint-validator"
]
}
}
}在终端中运行这条命令
claude mcp add design-constraint-validator -- npx -y design-constraint-validator粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"design-constraint-validator": {
"command": "npx",
"args": [
"-y",
"design-constraint-validator"
]
}
}
}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": {
"design-constraint-validator": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"design-constraint-validator"
]
}
}
}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7日
注册名称已通过 GitHub 账户 CseperkePapp 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(2.3.0)于5个月前进入注册中心。
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。
Mathematical constraint validator for design systems — ensuring consistency, accessibility, and logical coherence.
Design Constraint Validator (DCV) validates design constraints across token sets and styles:
This is not a schema linter; it's a reasoning validator for values and relationships.
# Local (recommended)
npm i -D design-constraint-validator
dcv belongs to an unrelated package)npx design-constraint-validator --help
After a local install, the shorter dcv bin is available (e.g. npx dcv --help).
Requirements: Node.js ≥ 18.x (ESM)
DCV validates your tokens against your constraints. From an empty directory:
# 1. Your design tokens (DTCG-style "$value")
cat > tokens.json <<'JSON' { "color": { "text": { "$value": "#888888" }, "bg": { "$value": "#999999" } } } JSON
cat > dcv.config.json <<'JSON' { "constraints": { "enableBuiltInWcagDefaults": false, "enableBuiltInThreshold": false, "wcag": [ { "foreground": "color.text", "background": "color.bg", "ratio": 4.5, "description": "Body text on background" } ] } } JSON
npx design-constraint-validator validate tokens.json --summary table
npx design-constraint-validator why color.text --tokens tokens.json --format table
npx design-constraint-validator graph --tokens tokens.json --format mermaid > graph.mmd
These one-offs use the full package name because the bare
dcvbin name on npm
belongs to an unrelated package. After
npm i -D design-constraint-validator,
use the shorter
npx dcv ….
Example output (validate):
validate: 1 error(s), 0 warning(s)
ERROR wcag-contrast color.text|color.bg @ Body text on background — Contrast 1.24:1 < 4.5:1 scope rules warnings errors
global 1 0 1
Exit code is 1 when violations are found, 0 when clean (use --fail-on off to always exit 0). The built-in WCAG/threshold defaults target the bundled example token ids, so disable them (as above) when validating your own token names.
import { validate } from 'design-constraint-validator';
// Synchronous. Point at files, or pass tokens / constraints inline. const result = validate({ tokensPath: './tokens.json', configPath: './dcv.config.json', // omit to auto-discover dcv.config.json in the cwd });
if (!result.ok) { for (const v of result.violations) { console.log([${v.ruleId}] ${v.message}); } process.exitCode = 1; }
See [API Reference](docs/API.md) for complete programmatic usage.
DCV ships a second binary, dcv-mcp, that exposes the validator over MCP stdio for agent clients. Add it to a Claude Desktop or generic MCP client config like this:
{
"mcpServers": { "dcv": { "command": "npx", "args": ["-y", "--package", "design-constraint-validator", "dcv-mcp"] } } }
The server exposes six read-only, JSON-returning tools:
validate - validate inline tokens or a tokensPath against inline constraints or a config file.why - explain provenance, aliases, dependencies, dependents, and alias chain for one token id.graph - return token dependency nodes and edges.list-constraints - enumerate the active constraints (WCAG pairs, thresholds, order/lightness scales, cross-axis) for the given input.explain - turn a violation into plain-English text plus machine-readable facts.suggest-fix - compute a verified satisfying value for a violation (WCAG color, threshold/monotonic boundary) without writing anything.The three derivation tools (list-constraints, explain, suggest-fix) stay read-only — they return suggestions; applying them is up to you (dcv set / dcv patch). See [AI Guide](docs/AI-GUIDE.md) for the full agent loop.
Tool failures are returned as structured JSON: { "ok": false, "error": { "code": "...", "message": "..." } }.
Conventional linters catch schema issues ("has a value, has a type"). DCV enforces relationships that matter to users and brand integrity:
This transforms tokens from "bags of numbers" into a formal design system.
| Feature | Schema Linters | DCV |
|---|---|---|
| Validates | JSON structure, types | Mathematical relationships, accessibility |
| Catches | Missing fields, wrong types | Contrast violations, hierarchy breaks |
| Purpose | Format compliance | Design system integrity |
| Examples | DTCG schema validator | WCAG checks, monotonic scales |
DCV is not affiliated with Anima's
design-tokens-validator(schema-focused).
DCV accepts token JSON (flat or nested) and optional policy JSON. Adapters normalize common ecosystems:
{alias} references, $extensions passthrough; non-sRGB spaces warn rather than mis-calculate; composite types out of scope). See examples/dtcg/Full adapter documentation: [Adapters](docs/Adapters.md)
DCV is the standalone validation engine — use it for any token system.
DecisionThemes (coming 2026) is a complete design system framework built on DCV:
DCV powers DecisionThemes' validation layer — but works perfectly standalone. Preview: www.decisionthemes.com
The Design Constraint Validator engine is based on a theming and validation method published as defensive prior art.
To understand the underlying architecture (Decision Themes / Value Themes, deterministic compute, post-compute validation and receipts):
These documents keep the method openly implementable and prevent patent lock-up.
DCV generates CycloneDX-compliant SBOMs for supply chain transparency:
npx @cyclonedx/cyclonedx-npm in project rootDownload:
dcv validate --receipt already emits environment + input content hashes today; cryptographic signing is the roadmap partConstraints, not conventions.
Design systems need mathematical guarantees. This validator:
只显示了开头部分;其余内容请在仓库中阅读