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)
Author, validate, edit, and render deterministic .zen design docs to PNG/PDF.
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": {
"zenith": {
"command": "npx",
"args": [
"-y",
"@zenitheditor/zenith-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add zenith -- npx -y @zenitheditor/zenith-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"zenith": {
"command": "npx",
"args": [
"-y",
"@zenitheditor/zenith-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": {
"zenith": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@zenitheditor/zenith-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7日
注册名称已通过 GitHub 账户 zenitheditor 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(0.0.8)于4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。
A design-document format and engine built for the age of AI agents.
Plain-text .zen design files that you can read, diff, review, validate, and let an agent safely edit — compiled deterministically to pixel-exact PNG, self-contained SVG, and print-ready PDF.
Install Quick start Features Validation Commands Agents
Every banner and diagram in this README is itself created using Zenith — source in assets/showcase/ .
Zenith is a plain-text format and engine for design files — posters, decks, books, social graphics, diagrams, and more. The idea is simple: design should work the way code does. You should be able to read it, diff it, review it, test it, and let an agent safely edit it.
A .zen file is human-readable KDL text. The engine parses it, validates it against a large diagnostic set, compiles it to a backend-neutral scene, and renders the same file to the same pixels every time — as a PNG, self-contained SVG, or print-ready PDF.
The sections below are collapsed to keep this page skimmable — click any heading's ▸ to expand it. Install and Quick start are open by default.
The recommended way to install the zenith CLI is the install script, which detects your platform and downloads the matching prebuilt binary from GitHub Releases.
Linux / macOS
curl -fsSL https://raw.githubusercontent.com/zenitheditor/zenith/main/scripts/install.sh | sh
Windows (PowerShell)
irm https://raw.githubusercontent.com/zenitheditor/zenith/main/scripts/install.ps1 | iex
Set ZENITH_INSTALL_DIR to change the install location (default ~/.local/bin):
ZENITH_INSTALL_DIR=/usr/local/bin \
curl -fsSL https://raw.githubusercontent.com/zenitheditor/zenith/main/scripts/install.sh | sh
cargo install zenith-tool # from crates.io (installs the `zenith` binary)
cargo install --git https://github.com/zenitheditor/zenith zenith-tool # from source
Thirteen library crates are published under their own names for Rust projects that build on the engine directly: zenith-geometry, zenith-session, zenith-core, zenith-layout, zenith-raster, zenith-scene, zenith-perception, zenith-zpx, zenith-render, zenith-tx, zenith-producers, zenith-pipeline, and zenith-editor. zenith-editor-wasm and zenith-editor-bench are not published.
npm install -g @zenitheditor/zenith-mcp
npx -y @zenitheditor/zenith-mcp --help
The npm package installs the matching prebuilt zenith binary from GitHub Releases and is focused on the MCP Registry launch path. The unscoped zenith binary remains the Rust CLI.
Download directly from GitHub Releases:
| Platform | Architecture | Asset |
|---|---|---|
| Linux | x86_64 | zenith- -linux-x64.tar.gz |
| Linux | aarch64 | zenith- -linux-arm64.tar.gz |
| macOS | x86_64 | zenith- -macos-x64.tar.gz |
| macOS | Apple Silicon | zenith- -macos-arm64.tar.gz |
| Windows | x86_64 | zenith- -windows-x64.zip |
zenith update # latest stable release
zenith update --pre # latest prerelease zenith update --version # a specific release tag, e.g. the ones on the Releases page
Verify with zenith --version.
For local development or source installs:
git clone --recurse-submodules https://github.com/zenitheditor/zenith
cd zenith cargo build --release cargo install --path zenith-cli # installs zenith to ~/.cargo/bin ./scripts/install.sh --local # installs the local build to ~/.local/bin
The release binary lands at target/release/zenith. No C toolchain or system libraries are required — the dependency graph is C-free and unsafe is forbidden workspace-wide.
zenith validate examples/hello.zen # report diagnostics (add --json for machine output)
zenith fmt examples/hello.zen # canonical, idempotent formatting zenith fix draft.zen --apply # apply machine fixes (tokens, typos) in one step zenith tokens examples/hello.zen # list design tokens and their resolved values zenith inspect examples/hello.zen # print the node tree (read-only) zenith render examples/hello.zen --png hello.png # render to PNG
zenith render examples/multipage.zen --all-pages out/ # one PNG per page zenith render examples/hello.zen --pdf hello.pdf # print-ready PDF zenith render examples/hello.zen --svg hello.svg # self-contained SVG zenith render examples/multipage.zen --svg page.svg --page 2 zenith render examples/multipage.zen --all-pages-svg svg-pages/ zenith render examples/hello.zen --scene scene.json # dump the scene IR
| Export flag | Output |
|---|---|
--svg OUT | Page 1 by default, with outlined text. |
--pdf OUT | Every page in one PDF by default. |
--page N | Selects one page for single-output flags, including PDF. |
--all-pages-svg DIR | Writes page-N.svg in document order. |
--raster-scale F | SVG/PDF fallback resolution. Finite 0 < F <= 4, default 1. |
--scale F | PNG output scale. Finite 0 < F <= 4, default 1. |
--deny render.svg_rasterized | Blocks SVG exports requiring raster fallback. |
--deny render.pdf_rasterized | Blocks PDF exports requiring raster fallback. |
--raster-scale changes fallback pixels without changing vector geometry or page dimensions. Existing bitmap assets retain source resolution.render.svg_rasterized and render.pdf_rasterized report pages, command ranges, and reasons. JSON includes structured rasterized regions.Vec retain compatibility emission. Use report APIs for strict errors.zenith render examples/multipage.zen --pdf book.pdf --raster-scale 2 --json
zenith render examples/hello.zen --svg hello.svg --raster-scale 2 --json
The smallest valid document:
zenith version=1 {
project id="proj.hello" name="Hello Zenith" tokens format="zenith-token-v1" { token id="color.bg" type="color" value="#f8fafc" token id="color.ink" type="color" value="#111827" token id="font.body" type="fontFamily" value="Noto Sans" token id="size.heading" type="dimension" value=(px)42 } styles {} document id="doc.hello" title="Hello Zenith" { page id="page.hello" w=(px)480 h=(px)160 { rect id="rect.bg" x=(px)0 y=(px)0 w=(px)480 h=(px)160 fill=(token)"color.bg" text id="text.hello" x=(px)24 y=(px)24 w=(px)432 h=(px)112 fill=(token)"color.ink" font-family=(token)"font.body" font-size=(token)"size.heading" { span "Hello Zenith" } } } }
See examples/ for runnable .zen files covering shapes, rich text, inline markdown (loaded from an external file), code blocks, images, frames/groups, multi-page documents and styles, plus the richer features — gradient, shadow, blur, filter, mask, table, flowchart (shapes + connectors), charts (bar/line/area/pie/donut/sparkline, with legends, value labels, and data binding), and anchors.
Code got source control, types, tests, and PRs. Design files got none of that — and that hurts people and blocks AI.
Code got source control, types, tests, and pull requests. Design files got none of that. They're opaque blobs — you can't diff them, you can't review a change, and the same file can render differently on different machines.
That's a problem for people, and it's a bigger problem for AI. Agents can already write code and open pull requests, because code is text they can read and reason about. Drop them into a design tool and they go blind. Ask an agent to "make the heading brand red and tighten the layout" and there's nothing safe to grab onto — no stable target, no validation, no preview, no way to check the result.
Zenith fixes that. The goal is to make design files as safe to automate as code:
The opposite of an image model: Zenith generates the editable design , not a flat picture.
This is the most common mix-up, so it's worth being blunt: Zenith is the opposite of an image model like Nano Banana, ChatGPT image, or Grok Imagine.
An image ge
只显示了开头部分;其余内容请在仓库中阅读