mcpField
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Archform

Archform

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github.com/himanshu-mamgainMedia & Design0Updated 1w ago

About Archform

Archform is an MCP server for rendering and validating diagrams as code in formats including Mermaid, PlantUML, Graphviz, and D2. It serves AI assistants in local stdio setups or through a remote Streamable HTTP endpoint, and can use a self-hosted Kroki service.

Key features

  1. 01Render diagram source as SVG or PNG
  2. 02Validate source and report parser errors
  3. 03List supported formats and examples
  4. 04Support Mermaid, PlantUML, C4, Graphviz, and more

Use cases

  1. 01Turn source code or schemas into diagrams
  2. 02Draw architecture and sequence diagrams
  3. 03Check diagram syntax and locate errors

MCP support

Archform exposes `list_formats`, `render_diagram`, and `validate_diagram` tools to MCP clients. Rendering uses Kroki, with configurable `KROKI_URL` for a self-hosted instance.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"himanshu-mamgain-archform": {
"command": "npx",
"args": [
"-y",
"@archform/mcp"
],
"env": {
"KROKI_URL": "<KROKI_URL>"
}
}
}
}
Run commandnpx -y @archform/mcp
Environment variables to set
KROKI_URLKroki server used for rendering. Defaults to the public https://kroki.io; set it to a self-hosted instance to keep diagram source private.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@archform/mcp0.2.2

Tools

3

Saved tool list · updated 1w ago

This tool takes no parameters.

Details

From the project README

@archform/mcp

An MCP server that lets AI assistants render and validate diagrams as code: Mermaid, PlantUML, C4-PlantUML, Graphviz, D2, Structurizr, BPMN and Excalidraw.

It works in Claude Desktop, Claude Code, Cursor and VS Code over stdio, and in ChatGPT developer mode as a remote Streamable HTTP endpoint.

Example

The assistant writes diagram source like this D2:

direction: right
user: User {shape: person}
ai: AI assistant
mcp: Archform MCP
kroki: Kroki {shape: cylinder}
user -> ai: "draw our architecture"
ai -> mcp: render_diagram
mcp -> kroki: diagram source
kroki -> mcp: SVG / PNG
mcp -> ai: image + share link

render_diagram turns it into an image and a share link. The image below is that link:

Rendered example: user, AI assistant, Archform MCP and Kroki

Try it: once Archform is connected, ask your assistant:

Draw a C4 context diagram of a URL shortener.

Or "Turn schema.prisma into an ER diagram", or "Draw a sequence diagram of our login flow".

Privacy: rendering is done by Kroki. By default, diagram source is sent to the public kroki.io service. If your diagrams describe anything you would not paste into a public website, self-host Kroki and set KROKI_URL.

Tools

All tools are read-only (readOnlyHint: true, openWorldHint: true).

ToolInputResult
list_formatsnoneEvery supported format with aliases, outputs, a docs link and a minimal valid example
render_diagramformat, source (≤ 100 KB), output (svg default, or png)SVG as an embedded image/svg+xml resource, or PNG as image content, plus a shareable kroki.io link. Invalid source returns isError: true with the parser message and line
validate_diagramformat, source{ valid, errors: [{ line?, message }] }. An invalid diagram is a normal result, not a tool error

Formats

FormatAliasesOutputs
mermaidsvg, png
plantumlsvg, png
c4plantumlsvg, png
graphvizdotsvg, png
d2svg
structurizrsvg, png
bpmnsvg
excalidrawsvg

Use it locally (stdio)

Requires Node.js 22 or later.

Claude Desktop: add to claude_desktop_config.json:

{
  "mcpServers": {
    "archform": {
      "command": "npx",
      "args": ["-y", "@archform/mcp"]
    }
  }
}

Claude Code:

claude mcp add archform -- npx -y @archform/mcp

Cursor: add to ~/.cursor/mcp.json (or .cursor/mcp.json in a project):

{
  "mcpServers": {
    "archform": { "command": "npx", "args": ["-y", "@archform/mcp"] }
  }
}

VS Code: add to .vscode/mcp.json:

{
  "servers": {
    "archform": { "type": "stdio", "command": "npx", "args": ["-y", "@archform/mcp"] }
  }
}

To use your own Kroki, add "env": { "KROKI_URL": "https://kroki.internal.example.com" } to the server entry.

Run it remotely (Streamable HTTP)

npx -y @archform/mcp --http --port 3000
  • POST /mcp: MCP traffic (stateless)
  • GET /health: health check

By default the server binds to 127.0.0.1 and rejects requests whose Host or Origin is not localhost (DNS rebinding protection). To expose it publicly, for example for ChatGPT developer mode, bind to all interfaces and name your public hostname:

HOST=0.0.0.0 ALLOWED_HOSTS=mcp.example.com TRUST_PROXY=1 RATE_LIMIT_PER_CLIENT=0 PORT=8080 npx -y @archform/mcp --http

Then add https://mcp.example.com/mcp as a connector in ChatGPT (Settings → Connectors, developer mode) with no authentication. Serve it behind HTTPS.

Limits

The HTTP endpoint limits request bodies to 512 KB and counts tool calls, not HTTP requests. A session's initialize and tools/list requests are not counted.

  • Overall: RATE_LIMIT (default 120 per minute) caps tool calls from all clients combined. Size it to what your Kroki backend can handle. The default is polite to the public kroki.io; raise it for a self-hosted Kroki.
  • Per client: RATE_LIMIT_PER_CLIENT (default 60 per minute) stops one client from using the whole budget. ChatGPT calls your server from OpenAI's servers, so all ChatGPT users share a handful of IPs, and this limit applies to them together. On a deployment that mainly serves ChatGPT, set RATE_LIMIT_PER_CLIENT=0 (as in the example above) or make it equal to RATE_LIMIT, so ChatGPT users can use the whole overall budget.
  • Behind a proxy: most hosts (Render, Fly.io, Railway, Cloud Run) put a proxy in front of your app, so every request seems to come from the proxy's address. Set TRUST_PROXY=1 (the number of proxies in front of the server) so the limiter uses the address your proxy added to X-Forwarded-For. Leave it unset when clients connect directly. Clients can write anything into that header, so it is ignored unless TRUST_PROXY is set.

When a single tool call is over a limit, the model gets a normal tool result with isError: true and a message like "Rate limit reached: … Retry in 42s.", so it can tell the user to wait. An HTTP 429 would show up only as a generic connector failure. Batched or malformed requests over the limit get 429 with Retry-After.

The counters are in memory, so each instance enforces its own limits.

Configuration

Flag / variableDefaultMeaning
--httpoffServe Streamable HTTP instead of stdio
--port, PORT3000HTTP port
--host, HOST127.0.0.1HTTP bind address
ALLOWED_HOSTSComma-separated Host values accepted on a public bind
RATE_LIMIT120Tool calls per minute across all clients; 0 disables
RATE_LIMIT_PER_CLIENT60Tool calls per minute per client IP; 0 disables
TRUST_PROXY0Number of reverse proxies whose X-Forwarded-For entries are trusted (true = 1)
KROKI_URLhttps://kroki.ioKroki server used for rendering and share links

Self-hosting Kroki

Kroki ships as Docker images. The core image covers PlantUML, C4-PlantUML, Graphviz, D2 and Structurizr; Mermaid, BPMN and Excalidraw each need a companion container:

# docker-compose.yml
services:
  kroki:
    image: yuzutech/kroki
    ports: ["8000:8000"]
    environment:
      KROKI_MERMAID_HOST: mermaid
      KROKI_BPMN_HOST: bpmn
      KROKI_EXCALIDRAW_HOST: excalidraw
  mermaid:
    image: yuzutech/kroki-mermaid
  bpmn:
    image: yuzutech/kroki-bpmn
  excalidraw:
    image: yuzutech/kroki-excalidraw
docker compose up -d
KROKI_URL=http://localhost:8000 npx -y @archform/mcp

Shareable links then point at your Kroki instance, so they only open for people who can reach it. See the Kroki installation docs for details.

Development

pnpm install
pnpm test                      # unit + in-process integration tests (Kroki stubbed)
KROKI_LIVE=1 pnpm test         # also run the live tests against KROKI_URL
pnpm build
npx @modelcontextprotocol/inspector node dist/index.js           # stdio
node dist/index.js --http      # then point the Inspector at http://127.0.0.1:3000/mcp

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.