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 40+ diagram types from text; render locally to SVG/PNG, open in the Diagrammo editor
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": {
"dgmo": {
"command": "npx",
"args": [
"-y",
"@diagrammo/dgmo-mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add dgmo -- npx -y @diagrammo/dgmo-mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"dgmo": {
"command": "npx",
"args": [
"-y",
"@diagrammo/dgmo-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"dgmo": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@diagrammo/dgmo-mcp"
]
}
}
}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 diagrammo 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(0.29.14)은 5일 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.
Turn a conversation into a real diagram — without leaving your AI tool.
This MCP server gives Claude (and any MCP-compatible AI tool) the ability to render sequence diagrams, flowcharts, ER diagrams, C4 architecture, gantt charts, and 40+ other chart types from concise text markup — then hand the result off to a full editor for refinement. Ask for a diagram in chat; get a real one back.
Ask in plain language — _"diagram the auth flow as a sequence"_, _"chart the Q3 plan as a gantt"_, _"draw our services as a C4 diagram"_ — and Claude writes the markup and renders it. The markup stays readable and diffable:
flowchart Mutiny Resolution
direction-tb
[Sail] Set sail under the captain {Trouble?} Discontent in the crew? {Vote} Crew vote called [Mutiny] Seize the ship
(Sail) -> (Trouble?) (Trouble?) -Yes-> (Vote) (Vote) -Mutiny-> (Mutiny)
→ renders to the flowchart above. All rendering happens locally — no diagram data leaves your machine.
| Tool | What it does | Over HTTP |
|---|---|---|
render_diagram | Render DGMO markup to SVG or PNG | yes |
validate_diagram | Check markup and report parse errors, without rendering | yes |
suggest_chart_type | Suggest the chart types that fit a description | yes |
list_chart_types | List all supported chart types, marking the beta ones | yes |
get_language_reference | Get DGMO syntax documentation for accurate generation | yes |
get_examples | Fetch worked examples for a chart type | yes |
| `share_diagram` | Get a shareable diagrammo.app URL — hand your diagram to the web editor | yes |
| `open_in_app` | Open the diagram straight into the Diagrammo desktop app for editing | no |
check_app_installed | Report whether the desktop app is installed | no |
preview_diagram | Render one or more diagrams and open an HTML preview in the browser | no |
generate_report | Build a polished multi-section HTML report with ToC and optional source | no |
put_space_diagram | Write a diagram into a Diagrammo Cloud space, by folder and title | yes |
get_space_diagram | Read a diagram from a Cloud space, by id or by folder and title | yes |
list_space_diagrams | List every diagram in a Cloud space | yes |
The three space tools send the diagram to Diagrammo Cloud. They need a space API key (minted on the Diagrammo account page) as DGMO_API_KEY in the server's environment — never as a tool argument, so the key stays out of the transcript.
share_diagram and open_in_app are the bridge out of chat: a diagram Claude generates becomes something you can refine, restyle, and embed — see below.
The four marked no open a browser or launch the desktop app. Over HTTP that would happen on the machine running the server rather than on yours, so they are not offered there — see Serving over HTTP.
The MCP server is one entry point into [Diagrammo](https://diagrammo.app) — a whole ecosystem built on the same DGMO markup. Generate in chat, refine in a real editor, embed anywhere:
open_in_app drops an AI-generated diagram straight into it, with live preview, palettes, and export.share_diagram URLs open right here.npx @diagrammo/dgmo-cli file.dgmo -o out.png, or install it with npm install -g @diagrammo/dgmo-cli (macOS and Linux).One markup, everywhere. A diagram you generate here renders identically in the
app, in your docs, and in Obsidian — because they all speak DGMO.
[→ Try it free at diagrammo.app](https://diagrammo.app)
Install the dgmo CLI and let it wire everything up:
npm install -g @diagrammo/dgmo-cli # macOS and Linux
dgmo install # auto-detects Claude Code, Codex, Claude Desktop, Cursor, …
Prefer Homebrew or pacman? See diagrammo.app/dev.
dgmo install configures each detected assistant non-interactively and points it at dgmo mcp, so there's no separate package to install or prompts to answer. Target one surface with dgmo install claude-code (or codex, claude-desktop, …).
Prefer to edit configs yourself? Point any MCP client at the server via npx (no global install needed):
Claude Code — .claude/settings.local.json; Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": { "dgmo": { "command": "npx", "args": ["-y", "@diagrammo/dgmo-mcp"] } } }
If you have the dgmo CLI installed, { "command": "dgmo", "args": ["mcp"] } works too. Restart the client after saving — the tools appear automatically.
The setups above launch the server as a child process and talk to it over its standard input and output. That needs the server and the client on the same machine. Where they are not — a hosted agent platform, a container, one server shared by several people — start it as an HTTP endpoint instead. The same endpoint also suits one machine, when you want a single server always running for every AI tool you use:
dgmo mcp --http # with the dgmo CLI installed
npx -y @diagrammo/dgmo-mcp --http # http://127.0.0.1:3333/mcp MCP_TRANSPORT=http MCP_PORT=8080 npx -y @diagrammo/dgmo-mcp
Every option takes a flag or an environment variable, whichever your setup can express:
| Flag | Variable | Default | What |
|---|---|---|---|
--http | MCP_TRANSPORT=http | off | Serve streamable HTTP instead of stdio |
--port | MCP_PORT | 3333 | Port to listen on |
--host | MCP_HOST | 127.0.0.1 | Interface to bind |
--path | MCP_PATH | /mcp | Path the endpoint answers on |
--allow-host | MCP_ALLOWED_HOSTS | loopback | Extra Host headers to accept, repeatable |
--allow-origin | MCP_ALLOWED_ORIGINS | own origin | Origin headers to accept, repeatable |
Each request is served independently — no sessions, nothing kept between calls — so one endpoint can serve several clients at once.
The server has no authentication of its own. It binds loopback by default and
rejects requests carrying a
Hostheader it was not told to expect, or anOrigin
header other than its own unless
--allow-originnames it, which is enough
for a client on the same machine or inside the same container. Anything reachable from
a wider network needs your own authentication in front of it, and
--allow-hostfor
the hostname it will be reached by. Binding a non-loopback interface without naming a
host prints a warning saying so.
--help prints all of this from the installed version.
Run the server as a background service so it starts at login and restarts if it stops. Change the dgmo path to what which dgmo prints.
Linux — systemd user service, saved as ~/.config/systemd/user/dgmo-mcp.service:
[Unit]
Description=Diagrammo MCP server over HTTP After=network.target
[Service] ExecStart=%h/.local/bin/dgmo mcp --http Environment=PATH=%h/.local/bin:/usr/local/bin:/usr/bin:/bin Restart=on-failure RestartSec=3
[Install] WantedBy=default.target
systemctl --user daemon-reload
systemctl --user enable --now dgmo-mcp
macOS — launchd agent, saved as ~/Library/LaunchAgents/app.diagrammo.mcp.plist:
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
Label app.diagrammo.mcp ProgramArguments
/opt/homebrew/bin/dgmo mcp --http
EnvironmentVariables PATH /opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin RunAtLoad KeepAlive
launchctl bootstrap gui/$(id -u) ~/Library/LaunchAgents/app.diagrammo.mcp.plist
Then point each AI tool at http://127.0.0.1:3333/mcp as a streamable-HTTP MCP server.
All rendering is local. Your diagram markup and the images it produces never leave your machine, except when you explicitly call share_diagram (which encodes the diagram into a diagrammo.app URL). See the privacy terms.
pnpm hub
One command, one server, one browser tab. The hub opens a tabbed shell over the three AI-tuning dev tools — switch between them with the top tabs, no separate ports or commands to remember:
suggest_chart_type, score prompts live, save back to triggers.json.claude -p. blocks in dgmo's language-reference.md, sliced into get_language_reference): pick a type, edit how the AI is told to style it, run a prompt against a committed dataset fixture (so inputs never move between runs), and see the generated DGMO + rendered image side by side. The picker doubles as a coverage bar; "Compare 3×" renders no-guidance vs your앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요