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)
Generate images and video from text prompts across Gemini, OpenAI and Kie AI.
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": {
"mediagen": {
"command": "npx",
"args": [
"-y",
"mediagen"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add mediagen -- npx -y mediagen프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"mediagen": {
"command": "npx",
"args": [
"-y",
"mediagen"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"mediagen": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"mediagen"
]
}
}
}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 Cripacx 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(0.3.0)은 3개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 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.
Give your agent image and video generation across Google Gemini, OpenAI and Kie AI — one skill, one install, no API to learn.
Say roughly what you want and let it decide the rest:
You: Make a hero image for the landing page, wide, something moody and industrial.
Agent: npx -y mediagen image "A disused loading dock at dusk, wet concrete…" --aspect-ratio 21:9 --json → ./output/image-20260823T094107Z.png
It wrote the prompt out in full, knew 21:9 rules OpenAI out, and read the path back from --json.
Or be exact, and it stops deciding:
You: Same thing with gemini-3-pro-image, 21:9, 4K, marked as AI-generated,
saved as hero.png.
Agent: npx -y mediagen image "A disused loading dock at dusk, wet concrete…" --model gemini-3-pro-image --aspect-ratio 21:9 --size 4K --output-name hero.png --json → ./output/hero.jpg
[looks at the image]
npx -y mediagen mark ./output/hero.jpg --visible-label --label-position top-right --json → ./output/hero.labelled.jpg
It marked in a second pass, after seeing where the subject ended up — and put the label on the flat roofline rather than over the loading bay.
Anything the CLI takes can be asked for in words — provider, model, aspect ratio, size, output name and directory, quality preset, video duration, an input image to edit, and whether to mark the result. Name any of them and the agent uses it as given; leave it out and it chooses, or falls back to what you configured.
npx -y skills add Cripacx/mediagen --skill mediagen
That is the whole installation. Nothing else to set up — the skill runs the CLI through npx, which fetches it on first use and caches it afterwards.
Then give it a key:
npx -y mediagen init
An interactive wizard: pick providers, enter each key without it being echoed, verify each against the live API, choose a model per provider, and set a default. One key is enough to start.
Now just ask your agent for an image.
mediagen models is there when you want to see what is available before choosingAnd what it will not do: ask you to paste an API key into the chat. On a configuration error the skill tells you to run mediagen init in your own terminal.
mediagen init covers first-time setup. To change something later:
npx -y mediagen config edit
A menu of every setting with its current value and where that value came from — provider, model, key, output directory, quality preset, and what mediagen mark does by default. Each change is written as you make it.
For CI and scripts, where there is no terminal, config set takes the same settings as arguments:
echo "$GEMINI_API_KEY" | npx -y mediagen config set gemini --stdin
npx -y mediagen config set gemini-model gemini-3-pro-image
Set the order you prefer, most preferred first:
npx -y mediagen config set provider-priority gemini,kie,openai
It is a preference, not a whitelist. A request with no --provider goes to the first provider in the order that has a key and can do the job — so a missing OpenAI key never stops something Gemini can do, and asking for video skips straight past the providers that do not make any. Naming --provider overrides all of it.
mediagen models shows the resulting order, which providers are usable, and what a request would get:
1. Google Gemini (gemini) [preferred, key from config file]
Would use: gemini-3.1-flash-image (provider default)
A request with no --provider uses gemini/gemini-3.1-flash-image.
With --json it adds wouldUse, usableProviders and providerPriority. That is what the skill has an agent read before generating, so it never picks a model from a provider you have no key for.
To check what is configured and whether the keys still work:
npx -y mediagen doctor
doctor reports, per provider, whether a key is configured, which layer it came from, and whether the provider accepts it — keeping _not configured_, _rejected_, _unreachable_ and _no cheap way to check_ distinct, because they call for four different fixes.
[!WARNING]
There is deliberately no flag that takes an API key as an argument. Arguments
land in shell history and in the process list, where they outlive the command
that used them.
Settings resolve from the environment first, then .env in the working directory, then the config file:
| Variable | Purpose |
|---|---|
GEMINI_API_KEY, OPENAI_API_KEY, KIE_API_KEY | credentials; at least one |
MEDIAGEN_PROVIDER_PRIORITY | providers in preference order |
GEMINI_MODEL, OPENAI_MODEL, KIE_MODEL | default model per provider |
MEDIAGEN_OUTPUT_DIR | where media is saved |
MEDIAGEN_QUALITY | fast, balanced, quality |
MEDIAGEN_MARK | mark output by default |
MEDIAGEN_VISIBLE_LABEL | add a visible label by default |
[!TIP]
A stale environment variable shadowing the key you just configured is the most
expensive failure this kind of tool has.
mediagen config listmarks every
shadowed value, so you can see it rather than guess.
| Provider | Images | Video | Editing | Key verification |
|---|---|---|---|---|
| Google Gemini | Nano Banana family, up to 4K | yes | yes | live probe |
| OpenAI | gpt-image family, DALL·E | — | yes | live probe |
| Kie AI | ~30 models: Flux, Imagen, Grok | — | most | no cheap probe; reported as such |
[!NOTE]
OpenAI takes pixel dimensions rather than aspect ratios and genuinely cannot
produce 16:9 — its widest image is 1536×1024, which is 3:2. Asking for 16:9 is
refused by name rather than quietly served as something else. The skill knows
this and routes wide shapes to Gemini.
A model absent from the listings is still sent to the provider, so a newly released model works before mediagen knows about it. Kie's catalogue is generated from Kie's own documentation rather than maintained by hand.
The EU AI Act splits disclosure into two duties, so mediagen mark has two independent switches:
| Flag | Duty | What it does |
|---|---|---|
| machine-readable | make it findable by tools | writes IPTC/XMP DigitalSourceType, on by default |
--visible-label | disclose it to people | composites the EU's official AI-content label |
Generating never marks. Marking is always a second command, run on the file afterwards:
npx -y mediagen image "a wide banner" --aspect-ratio 21:9 --size 2K --json
npx -y mediagen mark ./output/image-….jpg --visible-label --label-position top-left
That is not ceremony. A visible label has to go where the subject is not, and only the finished image can say where that is. The machine-readable marker is not free either: adding metadata to a JPEG or WebP means decoding and re-encoding it, so marking costs a second lossy pass — worth paying deliberately, not as a side effect of asking for an image. mediagen re-encodes at high quality to keep that cost small, but it cannot make it zero.
To have mediagen mark draw the visible label without being asked each time:
npx -y mediagen config edit # "AI marking by default"
A configured default is still overridable per run with --no-mark or --no-visible-label.
The visible label is the European Commission's own icon, published with the Code of Practice on Transparency of AI-generated Content and free to use without attribution. Two of its three variants are used: AI GENERATED by default, AI MODIFIED with --modified, for media a person made and a model altered. The light or dark version is picked from what is actually under the corner it lands in, because a label nobody can read is not a disclosure.
It sits bottom-right by default. --label-position moves it to another corner, or auto puts it wherever the image has the least detail.
[!IMPORTANT]
A visible label is never written over its source. `mediagen mark photo.png
--visible-label produces photo.labelled.png` and leaves the pixels of
photo.pngexactly as they were, while writing the machine-readable marker
into it — so whichever of the two you publish carries the disclosure. A label
placed badly can be redone from an untouched original and from nothing else.
--in-placeoverwrites if you really mean to.
The skill is a wrapper around a CLI, and the CLI stands on its own.
npx -y mediagen image "a wide banner" --aspect-ratio 21:9 --size 2K
npx -y mediagen image "make the sky stormy" --input ./photo.jpg npx -y mediagen mark ./output/image-….jpg --visible-label npx -y mediagen video "a marble rolling down a wooden track" --duration 6 npx -y mediagen models
Install it globally if you use it often enough to want the shorter command:
npm install -g mediagen
| Option | |
|---|---|
--provider | gemini, openai, kie |
--model | see mediagen models |
--input | source media to edit or transform |
--aspect-ratio | 1:1, 16:9, 9:16, … |
--size | 1K, 2K, 4K |
--duration | video only |
| --output-name | the
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요