Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
作者による説明(英語の原文)
Deterministic, non-generative image transform MCP server: reproducible recipes, immutable originals.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"atx": {
"command": "npx",
"args": [
"-y",
"atx-mcp",
"--workspace",
"your-value"
]
}
}
}ターミナルでこれを実行します
claude mcp add atx -- npx -y atx-mcp --workspace your-valueこのサーバーは起動コマンドに値を入れる必要があるので、設定をコピーして入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"atx": {
"command": "npx",
"args": [
"-y",
"atx-mcp",
"--workspace",
"your-value"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
このサーバーは起動コマンドに値を入れる必要があるので、設定をコピーして入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"atx": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"atx-mcp",
"--workspace",
"your-value"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント gridhra で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.6.7)がレジストリに届いたのは11 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにRust, Docker, Node.jsも必要です。
同じカテゴリには:Figma Context MCP, Claude Real Video, Figma MCP Server
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日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.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
English | 日本語 | 简体中文
A deterministic (non-generative) asset transformation MCP server for general-purpose AI agents, written in Rust.
It executes editing intent — "level the horizon, crop to 16:9, brighten it up a touch" — as a declarative transform recipe, and tracks every result as an immutable revision. The original asset is never modified.
Tilt correction + auto levels + a look applied (a fully deterministic recipe) — left: input / right: output.
See docs/DESIGN.md for the full design.
"Straighten this photo and crop it to a 16:9, 1600px eye-catch. WebP."
import_asset → detect_tilt (the AI skips correction when it's already near-level) → apply_transform (rotate → crop → resize → encode) → export_asset. The original is never touched, and the same recipe reproduces the same result every time.
"Generate the OGP, Instagram square, and thumbnail versions of this photo."
One original fans out into OGP 1200×630, Instagram 1080 square, and a 400px thumbnail in parallel. The same-recipe-same-revision idempotency means re-running never double-creates output; a one-word preset name works too.
"Strip the location data for sure, but don't touch the colors."
strip_metadata (exif) removes EXIF including GPS while keeping the ICC profile intact. The AI can also warn ahead of time by checking has_gps from inspect_image.
"Make just the sky bluer, leave everything else alone."
Covers curves / levels / hsl / white_balance, the film_soft preset, and importing your own .cube LUT with import_asset then applying it with lut.
"Darken just the sky a bit, keep the ground as is."
generate_mask builds a mask (gradient, luminosity range, or hue range); after wiring it into the adjustment, render_preview with overlay:"mask" shows exactly where it will bite before you commit.
"Blur a copy of this photo and blend it in at 50% screen for a soft glow."
The layers stack combines 16 blend modes, opacity, and masks to build reproducible composites like soft focus.
"Stamp my logo in the corner, remove the power lines, and fix the converging verticals."
svg_overlay burns in a logo, clone/heal remove blemishes or wires by compositing both texture and tone, and perspective corrects converging verticals.
"Read this receipt photo for me." / "What does this slide say?"
detect_document finds the page or screen and returns a perspective quad ready to paste, the ocr_document preset (grayscale, auto levels, light sharpen) and trim concentrate the pixel budget on the text, and render_preview with long_edge:1568 hands the model an image it can actually read. No OCR engine is bundled: the model does the reading, atx only makes the pixels legible and reproducible. detect_text_blocks answers the question that decides the rest — "will this text survive the downscale, and where do I cut?" — by returning the text blocks in reading order plus ready-to-paste crop bands. threshold (Otsu / Sauvola) and ocr_binarize exist for external OCR engines.
"Show me this image before and after the edits, side by side."
compare_revisions places before/after side by side, or returns a difference heatmap with stats like mean_abs_diff. Every revision keeps its lineage, so the full edit history behind any image used in an article can be traced and reproduced — byte-identical on any machine.
"Two mounted sheets were scanned on one page. Save them as two files, keeping a little of the mount around each."
There is no split operation: the model runs one apply_transform per piece against the same source revision (crop to the left/right half, then trim to snap to the paper's edges with padding to keep some mount), and export_asset writes both at once. render_preview with no recipe and overlay:"grid" is how it reads the coordinates first.
What atx doesn't do — generative editing, RAW development, ML-based auto-cropping, OCR itself — is out of scope; see docs/DESIGN.md for the roadmap.
atx-mcp is a single self-contained binary with no runtime dependencies. Pick one of the following.
cargo binstall atx-mcp
claude mcp add --scope user asset-transform -- atx-mcp --workspace /path/to/asset-workspace
cargo-binstall downloads the release archive built by this repository's CI instead of compiling, so this is the fastest route for anyone who already has a Rust toolchain. (--scope user makes the server available in every project; omit it for the current project only.)
cargo install atx-mcp
Works on any platform a Rust toolchain supports, including ones without a prebuilt binary. Needs a C compiler as well (libwebp is built from its vendored source).
Installer scripts (default install location is ~/.local/bin, or %LOCALAPPDATA%\Programs\atx-mcp on Windows; the archive is verified against SHA256SUMS before extraction):
# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/gridhra/atx-mcp/main/scripts/install.sh | sh
# Windows
irm https://raw.githubusercontent.com/gridhra/atx-mcp/main/scripts/install.ps1 | iex
To download manually, grab atx-mcp- - .tar.gz (.zip on Windows) from Releases. Supported targets:
| Platform | Target triple |
|---|---|
| macOS (Apple Silicon) | aarch64-apple-darwin |
| macOS (Intel) | x86_64-apple-darwin |
| Linux x86_64 | x86_64-unknown-linux-musl (statically linked, no glibc required) |
| Linux arm64 | aarch64-unknown-linux-musl (statically linked, no glibc required) |
| Windows x86_64 | x86_64-pc-windows-msvc |
claude mcp add asset-transform -- ~/.local/bin/atx-mcp --workspace /path/to/asset-workspace
ghcr.io/gridhra/atx-mcp is a FROM scratch image holding the statically linked binary and nothing else (linux/amd64 and linux/arm64).
claude mcp add asset-transform -- \
docker run -i --rm -v "$PWD:/workspace" ghcr.io/gridhra/atx-mcp:0.6.7
Two things to keep in mind. `-i` is required: the server speaks the MCP stdio transport and needs stdin to stay open. And paths are container paths: the directory you bind-mount appears as /workspace inside the container, so import_asset and export_asset take paths like /workspace/photos/shot.jpg, not host paths.
The prebuilt native binary for your platform is pulled in automatically via optionalDependencies.
claude mcp add --scope user asset-transform -- npx -y atx-mcp --workspace /path/to/asset-workspace
Or add it directly to your MCP client config:
{
"mcpServers": { "asset-transform": { "command": "npx", "args": ["-y", "atx-mcp", "--workspace", "/path/to/asset-workspace"] } } }
--workspace (env: ATX_WORKSPACE) is the directory used as the asset store. It is created automatically if it doesn't exist.
--max-concurrent-tools (env: ATX_MAX_CONCURRENT_TOOLS) caps how many pixel-processing tool calls (import / inspect / detect_* / apply / preview / compare / mask) run at the same time; the default is the CPU count. Lower it on memory-constrained hosts. Tool work runs on dedicated threads, so the stdio transport (ping, cancellation, the vocabulary tools) stays responsive while images are being processed.
| Tool | Role | |||
|---|---|---|---|---|
list_operations | Compact catalog of the recipe vocabulary: every operation with a one-line description and terse parameter hints, plus the built-in preset names. Optional `category:"geometry"\ | "color"\ | "filter"\ | "output"` narrows it (read-only) |
explain_operation | Full reference for one operation: parameter table (type, range, required/default, semantics), ready-to-paste JSON examples and gotchas. A built-in preset name works too and returns its full operation list. An unknown name returns the valid operations and presets, grouped (read-only) | |||
import_asset | Import a local image into the workspace (sha256-idempotent). Takes path for one file or paths for a batch of up to 64 (a failing file does not abort the batch). Warns via already_derived_from when the bytes are already the output of a recipe in this workspace | |||
inspect_image | Inspect dimensions, EXIF summary, ICC profile, presence of GPS data, luma statistics, a sharpness score (variance of the Laplacian — relative, so compare it against a known-good capture of the same subject) and a perceptual_hash (dHash, 16 hex digits) for "is this the same picture?". include_exif:true additionally returns every EXIF field as {ifd, tag, value} entries — off by default because the full dump can carry GPS coordinates and names (read-only) | |||
detect_tilt | Estimate tilt angle via Canny+Hough (coarse) plus a projection profile (sub-0.1° refinement). Also returns horizontal/vertical family estimates; the full score curve is opt-in via include_score_curve:true. Returns "do not correct" when confidence is low (read-only) | |||
detect_document | Find the dominant quadrilateral (page, screen, whiteboard, sign) via Canny + contours and return it as a perspective-ready quad (tl, tr, br, bl) with confidence, area_ratio, an output_size_hint and a paste-ready suggested_operation. Returns quad:null with a reason (no_quad_found / already_rectified / low_confidence) rather than guessing (read-only) | |||
detect_text_blocks | Find the text-like blocks (headline, paragraphs, table, caption) via Otsu binarization + run-length smearing + connected components, in reading order, each with line_count, median_line_height_px and ink_ratio. legibility.line_height_at_1568_px says whether the text survives a downscale to long edge 1568 (below ~16px it usually does not), and legibility.recommended_bands splits the image into horizontal bands that clear that bar — each entry is already a crop operation to paste before render_preview (read-only) | |||
generate_mask | Generate a deterministic grayscale mask (linear_gradient / radial_gradient / luminosity_range / color_range) as a PNG revision with the same dimensions as the reference image, to be referenced from an operation's mask field (idempotent) | |||
render_preview | Apply a recipe (or a preset) at low resolution (long edge ≤768 by default, long_edge 256..1568 to hand a vision model a legible page) and return it as an inline image. `overlay:"grid"\ | "thirds"\ | "horizon" overlays composition guide lines, and overlay:"mask" (with mask_revision_id) tints the coverage of a mask (drawn on the preview only; it has no effect on the actual transform). Also reports estimated_vision_tokens, a rough width*height/750` budget for the returned image |
| apply_transform | Apply a recipe (or a preset) at full resolution and pr
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