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)
Headless MCP: author and render PromoShot .promo video projects (stills, GIFs, video).
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": {
"promoshot": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/garalex/promoshot-mcp:v0.4.7"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add promoshot -- docker run -i --rm ghcr.io/garalex/promoshot-mcp:v0.4.7프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"promoshot": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/garalex/promoshot-mcp:v0.4.7"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"promoshot": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/garalex/promoshot-mcp:v0.4.7"
]
}
}
}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 GarAlex 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(0.4.7)은 9일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Docker도 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.
See it work: demo.md — technique prompts and open-ended creative briefs, each given to a fresh agent with only the skill and the MCP, its result beside the hand-built reference. The suite is in demos/.
The rendering engine behind PromoShot (App Store), and an open implementation of its project format. A .promo project is a folder — metadata.json plus its media — and this workspace is everything needed to validate, inspect, and render one to stills, image sequences, or mp4 with mixed audio: no app attached, byte-for-byte the same compositor the apps ship.
The design bet is that the format is the interface. An assistant, a script, or a person writes metadata.json; the engine renders it the same everywhere — the Mac and iOS apps (Metal + VideoToolbox), this repo's CLI, or a headless Linux box with no GPU at all (wgpu on lavapipe, ffmpeg as a subprocess). The format has three faces behind one truth: an authoring subset with four validated recipes (promo schema), the full document (--full), and a types-only JSON Schema generated from the parser's own structs (--types) — and the parser the validator runs is the parser the renderers use, so "validates" means "renders".
This is the open engine (and App Store app) for [PromoShot](https://promoshot.app) by Alexander Garmash — a full layer / keyframe / timeline editor for screen recordings, video, and photos (Mac; iPhone and iPad open the same .promo projects).
It is not the similarly named App Store apps that turn screenshots into template videos (for example “PromoShot: Screenshot to Video”). If you want keyframes, masks, GPU compositing, and an open .promo format, you want this one: promoshot.app · App Store.
Consumer apps: promoshot.app · App Store. Software directories can ingest this PAD 4.0 file:
https://raw.githubusercontent.com/GarAlex/promoshot/main/promoshot.pad.xml
| Crate | What it owns |
|---|---|
promo-model | The format: wire structs, migrations, palette roles, schema.md |
promo-timeline | Timeline math: keyframes, trims, attachments, waits, validation |
promo-gpu | wgpu compositing: quads, borders, letterbox, vectors, color conversion |
promo-text | Caption shaping and effects (cosmic-text) |
promo-engine | Preview/export orchestration, frame cache, memory governor, PCM mixer |
promo-media | Decoder/encoder trait registry; ffmpeg-subprocess backend + conformance suite |
promo-editor | The document's edit vocabulary: commands with undo, the wizard's arrangement, theme rules — what promo_apply and promo_slideshow are built on |
promo-cli | promo — render a project from the command line |
promoshot-mcp | MCP server over stdio, for agents |
./check-all.sh # fmt, clippy -D warnings, all tests, release build
Rendering video needs ffmpeg (and ffprobe) on PATH — frames are composited on the GPU and piped to it raw; ffmpeg only decodes and encodes. On a headless Linux machine, mesa-vulkan-drivers (lavapipe) is enough of a GPU.
The Linux release tarball carries its own CPU renderer: a lavapipe/ folder (Mesa's lavapipe, its libraries and a Vulkan loader; licences in lavapipe/NOTICES.txt) beside promo. Keep the folder next to the binary and a machine with no Vulkan driver at all — a fresh container, or a redeploy that dropped mesa-vulkan-drivers — still renders. promo picks, in order: a real GPU; the system's lavapipe; the bundled lavapipe (saying so once on stderr); SwiftShader, with a warning, since it is 10–50× slower than lavapipe. With none of them it lists what it tried and the fix (sudo apt-get install -y mesa-vulkan-drivers libvulkan1). PROMO_LAVAPIPE= points at the folder elsewhere; PROMO_LAVAPIPE=off never uses it. The binaries need glibc 2.39 (Ubuntu 24.04, Debian 13). A machine with no fonts at all still renders: text falls back to a built-in face (Tuffy) with a warning; install fonts-dejavu-core and fonts-liberation for real ones.
What a CPU costs, measured on a 703-frame 1080×1920 reel with narration:
| Renderer | promo video |
|---|---|
| Mac GPU (Apple silicon, Metal) | 13.7 s |
| lavapipe, 8 cores | 18.9 s |
| lavapipe, 2 cores | 77 s |
| lavapipe, 1 core | 179 s |
(lavapipe in an amd64-emulated ubuntu:24.04 container; SwiftShader took about half an hour on a comparable reel.)
promo video survives being killed. It encodes in segments (10 s of video each; --segment ) into .promo-parts/ beside the output, and the same command run again skips the finished segments and goes on from there, saying how many it found. The segments are joined without re-encoding, the soundtrack is mixed once for the whole range and muxed in at the join — no edge in the sound, in sync to the sample — and the folder is removed on success. The folder is keyed by a digest of the project's files and the render settings, so an edited project starts over instead of reusing stale segments. --from/--to take seconds (12.5) or frames (375f), to split a long render into chunks.
cargo build --release -p promo-cli # -> target/release/promo
promo schema # authoring subset + recipes; --full, --types promo validate [--strict] # "NOT OK" lists what will not render; --strict exits 1 on any finding promo inspect # canvas, layers, missing media, undefined colours promo upsert-keyframe --args '{"layer":"bg","time":2,"zoom":1.2}'
promo still --out f.png --time 2.5 promo frames --out frames/ --fps 30 --from 0 --to 4 promo video --out out.mp4 --fps 30 # resumable; --from/--to 12.5 or 375f
Add --json to any project command for machine output — one object on stdout, errors included, exit codes unchanged.
promo video mixes the soundtrack the apps would: trims and media cuts, held frames, speed with pitch preserved, keyframed volume, a focused narration ducking everything under it, and only the audio tracks the project keeps.
Headless renders are CLEAN — no watermark, and no license, serial or key will ever be asked for. (The Mac and iOS apps watermark free-tier renders; that is their App Store Pro line, and it stays on their side of the fence.)
promoshot-mcp speaks Model Context Protocol over stdio, so any MCP client can author, inspect and render projects. It owns no rendering code — every render shells to promo (found next to the executable, or on PATH, or via --promo), so the CLI stays the single contract.
Two pieces: the MCP server (tools) and the skill (workflow). Neither is vendor-specific. Agents do not find this repo by themselves.
The apps open what this engine writes from PromoShot 3.0 on. The App Store's 2.3.1 reads projects up to minReaderVersion 17 (no nested compositions, no 3D) and cannot be launched with --mcp-stdio.
1. Build — or don't
cargo build --release -p promo-cli -p promoshot-mcp
scripts/install-local.sh # …and on PATH, linked from target/release,
No Rust toolchain? Grab the prebuilt pair from Releases (linux-x64, macos-arm64; each asset's sha256 is on the release), or pull the image: docker pull ghcr.io/garalex/promoshot-mcp — both carry promo and promoshot-mcp together.
Put both on PATH, or pass --promo to the server. Rendering video also wants ffmpeg/ffprobe on PATH. promo skill install then puts the agent skill where every agent tool on the machine reads it — the shared ~/.agents/skills (Codex, Gemini CLI, Cursor, GitHub Copilot, Grok Build) and the own folder of each tool found that reads only its own (Claude Code, Antigravity); promo skill prints it.
2. MCP (required for tools)
Claude Code / Cursor / any mcp.json:
{
"mcpServers": { "promoshot-headless": { "command": "/ABS/PATH/target/release/promoshot-mcp", "args": ["--workspace", "/ABS/PATH/Promo", "--root", "/ABS/PATH/Promo"] } } }
--workspace is where new projects go; --root fences which projects the server will touch — pointing both at one folder is the tidy setup. Both optional. --log appends one line per tool call — when, which tool, how many milliseconds, how it went — for a session's own accounting; the demo pages are built from it.
Client one-liners:
# Claude Code
claude mcp add promoshot-headless /ABS/PATH/target/release/promoshot-mcp
grok mcp add promoshot-headless -- /ABS/PATH/target/release/promoshot-mcp \ --workspace /ABS/PATH/Promo --root /ABS/PATH/Promo grok inspect # confirms the server registered
docker build -t promoshot-mcp .
On a Mac with PromoShot 3.0 installed, the app is a server too — the same tools, with the person's open document, undo and approvals behind them. Switch Automation on in PromoShot's Settings, then register the app itself; it launches when the client starts it:
claude mcp add promoshot -- "/Applications/PromoShot.app/Contents/MacOS/PromoShot" --mcp-stdio
Settings ▸ Automation shows that line with this Mac's own path, and the URL form for clients that take one. promoshot names the app and promoshot-headless the binary, so both can be registered side by side; the skill asks which to use when both are there.
3. Skill (the workflow)
Same file everywhere: skill/SKILL.md.
REPO=https://github.com/GarAlex/promoshot
git clone --depth 1 $REPO /tmp/promoshot
mkdir -p ~/.claude/skills/promoshot cp /tmp/promoshot/skill/SKILL.md ~/.claude/skills/promoshot/SKILL.md
mkdir -p ~/.grok/skills/promoshot cp /tmp/promoshot/skill/SKILL.md ~/.grok/skills/promoshot/SKILL.md
mkdir -p ~/.agents/skills/promoshot cp /tmp/promoshot/skill/SKILL.md ~/.agents/skills/promoshot/SKILL.md
mkdir -p .cursor/rules cp /tmp/promoshot/skill/SKILL.md .cursor/rules/promoshot.md
Any agent that reads instructions can be handed the file directly; it assumes only these tools (or the CLI).
4. Verify — ask the agent for a render:
Render examples/ProductCard.promo to a still at 3s.
One promo_validate, one promo_render_still, and a device-framed app demo comes back as a path. From there, "make me a promo for " is the loop the skill teaches.
Tools: promo_schema (authoring subset + four validated recipes; promo_schema_full is the whole format; promo_schema_types is the format as a generated, types-only JSON Schema — also checked in at docs/promo.schema.json for $schema editor au
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요