Figma MCP

io.scalably由 scalably.io 發布v1.0.1更新於 Oct 24, 2026

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

發布方自己的服務：Figma MCP Server

已驗證STDIO僅桌面檔案與儲存媒體與設計

服務詳細資訊

識別碼: io.scalably/figma-mcp
傳輸: STDIO
執行環境: Desktop only
信任: Verified
發布於: Sep 16, 2026

安全提示

MCP 服務會接收對話中的工具呼叫請求，並可能執行外部操作。啟用前請先了解該服務能做什麼，並只授予它所需的權限。

註冊中心已驗證此服務的命名空間屬於 scalably.io。它以軟體包形式執行，而不是託管端點，因此沒有可與該網域比對的端點。

SourceWeft 的檢查只涵蓋條目是否完整、端點是否回應；不審查伺服器的程式碼，也不審查其工具的行為。

在 README 中新增徽章

向讀者展示該 MCP 服務已收錄於 SourceWeft，徽章連結到本頁。

[![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)

概覽

AI 產生的概覽

在本機執行的 Figma REST API MCP 伺服器，讓助理列出檔案中的頂層畫框，並下載某個畫框的 PNG、節點 JSON 與圖片填色對應表。

可以做什麼: 列出並篩選 Figma 檔案中的頂層畫框，包含頁面、名稱、節點 ID、尺寸與檔案中繼資料
取得單一畫框，並將 frame.png、node.json 與正規化的圖片填色對應表寫入本機輸出目錄
以結構化 JSON 回傳結果，包含狀態、摘要、目標、結果、佐證、警告與復原資訊
適用情境: 適合需要讓助理檢視 Figma 檔案的畫框結構，或把某個畫框的算圖與節點資料拉到本機專案，用於審閱、交付或後續處理時使用。
執行需求: 透過 npx 或 .mcpb 桌面套件以 stdio 在本機執行，僅支援桌面端。需在 FIGMA_TOKEN 提供具備 file_content:read 權限的 Figma 個人存取權杖，以及 Figma 檔案網址中的檔案 key。選用設定包括 API 基礎網址、請求逾時、JSON 與 PNG 大小上限及輸出根目錄。

安裝前請注意

未設定 FIGMA_TOKEN 時伺服器仍會啟動，但所有呼叫都會失敗。fetch_frame 會在輸出根目錄下寫入檔案，使用前請確認該路徑。畫框包與圖片填色對應表會保存在本機，fills.json 中的連結會在 14 天內過期。FIGMA_ALLOW_HTTP_FOR_TESTS 僅供測試套件使用，正常使用時不應設定。

安裝

在 SourceWeft 中

開啟儀表板中的 Figma MCP，將其新增到工作區。
為需要使用其工具的對話啟用該服務。

Desktop only，透過 STDIO。 STDIO 服務會啟動本機處理程序，因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照儲存庫中的啟動說明。

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