Figma MCP

io.scalably由 scalably.io 发布v1.0.1更新于 Oct 24, 2026

Figma MCP: list frames in a file and fetch one frame's JSON and image safely. 2 tools.

发布方自己的服务：Figma MCP Server

已验证STDIO仅桌面文件与存储媒体与设计

服务详情

标识符: io.scalably/figma-mcp
传输: STDIO
运行时: Desktop only
信任: Verified
发布于: Sep 16, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此服务的命名空间属于 scalably.io。它以软件包形式运行，而不是托管端点，因此没有可与该域名比对的端点。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

[![Listed on SourceWeft](https://sourceweft.com/badge/mcp/io.scalably%2Ffigma-mcp.svg)](https://sourceweft.com/mcp/io.scalably%2Ffigma-mcp?utm_source=readme&utm_medium=badge)

概览

AI 生成的概览

本地运行的 Figma REST API MCP 服务器，让助手列出文件中的顶层画框，并下载某个画框的 PNG、节点 JSON 和图片填充映射。

可以做什么: 列出并筛选 Figma 文件中的顶层画框，包含页面、名称、节点 ID、尺寸和文件元数据
获取单个画框，并将 frame.png、node.json 和规范化图片填充映射写入本地输出目录
以结构化 JSON 返回结果，包含状态、摘要、目标、结果、证据、警告和恢复信息
适用场景: 适合需要让助手查看 Figma 文件的画框结构，或把某个画框的渲染图和节点数据拉到本地项目，用于评审、交付或后续处理时使用。
运行要求: 通过 npx 或 .mcpb 桌面包以 stdio 在本地运行，仅支持桌面端。需要在 FIGMA_TOKEN 中提供具备 file_content:read 权限的 Figma 个人访问令牌，以及 Figma 文件 URL 中的文件 key。可选配置包括 API 基础地址、请求超时、JSON 与 PNG 大小上限和输出根目录。

安装前请注意

未设置 FIGMA_TOKEN 时服务器仍会启动，但所有调用都会失败。fetch_frame 会在输出根目录下写入文件，使用前请确认该路径。画框包和图片填充映射会保存在本地，fills.json 中的链接会在 14 天内过期。FIGMA_ALLOW_HTTP_FOR_TESTS 仅供测试套件使用，正常使用时不应设置。

安装

在 SourceWeft 中

打开控制台中的 Figma MCP，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照仓库中的启动说明。

README

Figma MCP

Figma REST API MCP server. 2 tools list the top-level frames in a file and fetch one frame's rendered PNG plus its node JSON and image-fill map into a local output directory.

Install

Claude Code:

Codex:

Claude Desktop: download figma-mcp.mcpb from the latest GitHub release and open it.

Setup

Create a personal access token at figma.com under account settings, with file_content:read scope.
Find the file key in any Figma file URL: it is the segment after /design/ or /file/, for example https://www.figma.com/design/AbC123xyz/My-file gives AbC123xyz. Both tools take it as file_key.
If FIGMA_TOKEN is not set the server still starts and lists its tools; every call then fails with FIGMA_NOT_CONFIGURED until the token is provided.
fetch_frame is the only tool that writes files (so it is not marked read-only); it writes frame.png, node.json, and fills.json into a directory below the configured output root (default ./figma-output). The other tool is fully read-only.

Tools (2)

Tool	What it does
`list_frames`	List and optionally filter all top-level FRAME nodes in a Figma file, with exact page/name/node-id data, dimensions, and file metadata
`fetch_frame`	Fetch one Figma frame and transactionally write frame.png, node.json, and a normalized image-fill map into a directory below the output root

Configuration

Variable	Required	Purpose
`FIGMA_TOKEN`	yes	Figma personal access token with `file_content:read`
`FIGMA_API_BASE_URL`	no	Override the Figma REST API base URL (default `https://api.figma.com/v1`)
`FIGMA_REQUEST_TIMEOUT_MS`	no	Per-request timeout in milliseconds (default 60000)
`FIGMA_MAX_JSON_BYTES`	no	Upper bound on a Figma JSON response, in bytes (default 104857600)
`FIGMA_MAX_FRAME_BYTES`	no	Upper bound on a downloaded frame PNG, in bytes (default 104857600)
`FIGMA_OUTPUT_ROOT`	no	Directory that every `fetch_frame` output directory must stay below (default `./figma-output`, created on first use)
`FIGMA_ALLOW_HTTP_FOR_TESTS`	no	Test suite only: `1` allows a plain-HTTP loopback API base. Never set it in normal use

Reply shape

Every tool returns plain JSON with status (succeeded, partial, no_op), summary, target, result, proof, warnings, recovery. Failures throw a plain error string: <code>: <message> <hint>.

Limits

fetch_frame renders through Figma's image API, so very large frames or extreme scale values can exceed the configured JSON or frame byte limits; lower scale or split the frame if that happens. fills.json URLs are temporary and expire within 14 days.

Verify

Each release lists the package version, the .mcpb sha256 and the production commit it was derived from in CHANGELOG.md. CI runs the tests and a clean install of the packed tarball on every push.

Privacy Policy

This server runs locally, on your machine, under your own credentials. It collects no personal data, contains no telemetry, stores nothing persistently beyond the frame bundles you explicitly fetch, and talks only to the vendor API it wraps. No third party, including Scalably, receives your data. Contact: [email protected]. Canonical copy: https://scalably.io/connector-privacy.html