
Dgmo Mcp
io.github.diagrammo由 GitHub 帳號 diagrammo 發布v0.29.15更新於 Nov 8, 2026
從文字產生40多種圖表類型;在本機渲染為SVG/PNG,並在Diagrammo編輯器中開啟
AI 翻譯由 AI 模型根據下方的原文描述翻譯,可能有誤。
原文描述
Generate 40+ diagram types from text; render locally to SVG/PNG, open in the Diagrammo editor
概覽
Dgmo 的本機 MCP 服務;無需登入;STDIO。
暫無 AI 概覽:以下資訊來自註冊中心條目和服務本身。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Dgmo Mcp,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
@diagrammo/dgmo-mcp
Turn a conversation into a real diagram — without leaving your AI tool.
[npm version] [npm downloads] [license]
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.
What you can do
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:
→ renders to the flowchart above. All rendering happens locally — no diagram data leaves your machine.
Tools
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.
Beyond the MCP server
The MCP server is one entry point into Diagrammo — a whole ecosystem built on the same DGMO markup. Generate in chat, refine in a real editor, embed anywhere:
- diagrammo.app — the desktop app.
open_in_appdrops an AI-generated diagram straight into it, with live preview, palettes, and export. - online.diagrammo.app — a full editor in the
browser, zero install.
share_diagramURLs open right here. - Docs integrations — drop DGMO fenced code blocks into your docs site: remark-dgmo, astro-dgmo, docusaurus-plugin-dgmo, fumadocs-dgmo.
- Obsidian — the Diagrammo Diagrams community plugin renders DGMO in your vault.
- CLI —
npx @diagrammo/dgmo-cli file.dgmo -o out.png, or install it withnpm 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
Setup
Easiest — one command
Install the dgmo CLI and let it wire everything up:
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, …).
Manual configuration
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:
If you have the dgmo CLI installed, { "command": "dgmo", "args": ["mcp"] } works too. Restart the client after saving — the tools appear automatically.
Serving over HTTP
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:
Every option takes a flag or an environment variable, whichever your setup can express:
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 anOriginheader 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.
Keep it running
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:
macOS — launchd agent, saved as ~/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.
Privacy
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.
Dev hub (AI-tuning tools)
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:
- Trigger tuning — edit the phrase/concept vocabulary that drives
suggest_chart_type, score prompts live, save back totriggers.json. - LLM judge — judge chart-type descriptions against prompts with
claude -p. - Guidance studio — author the per-type styling guidance the server
delivers (the
<!-- TIPS -->blocks in dgmo'slanguage-reference.md, sliced intoget_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 your tips for a by-eye check; Save validates and writes back tolanguage-reference.md.
These tools are dev-only and never bundled into the published server. (The
standalone pnpm harness and pnpm studio scripts still run a single tool each
if you ever want one in isolation.)
Contributing & releases
Development setup and the release workflow live in CONTRIBUTING.md.
License
MIT
來源:README.md,提交 60c2983
工具
0版本歷史
11- v0.29.14Nov 3, 2026
- v0.29.13Oct 29, 2026
- v0.29.12Oct 26, 2026
- v0.29.11Oct 20, 2026
- v0.29.10Oct 14, 2026
- v0.29.9Oct 10, 2026
- v0.29.7Oct 8, 2026
- v0.29.6Oct 1, 2026
- v0.29.5Sep 30, 2026
- v0.29.4Sep 20, 2026
- v0.29.3Sep 16, 2026
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