Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
제작자가 쓴 설명(영어 원문)
Deterministic, non-generative image transform MCP server: reproducible recipes, immutable originals.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"atx": {
"command": "npx",
"args": [
"-y",
"atx-mcp",
"--workspace",
"your-value"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add atx -- npx -y atx-mcp --workspace your-value이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"atx": {
"command": "npx",
"args": [
"-y",
"atx-mcp",
"--workspace",
"your-value"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"atx": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"atx-mcp",
"--workspace",
"your-value"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 gridhra 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.6.7)은 11일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Rust, Docker, Node.js도 있어야 해요.
같은 카테고리에는: Figma Context MCP, Claude Real Video, Figma MCP Server
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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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