Datadef MCP

Data-architecture diagrams your AI generates, edits, and exports — from Claude, Cursor, VS Code, Gemini CLI, or any MCP client. Typed tables with columns, pipelines, column-level lineage, 2,000+ real tool icons. Exported PNGs come back inline in the chat.

This repo is the public home of the remote MCP server at `https://datadef.io/mcp` (Streamable HTTP). There is nothing to install from here — it exists for directories, install links, and issues.

What it draws — live

The diagram below is not a screenshot. It is a public Datadef diagram embedded by URL — when the diagram is edited, this image updates with it. That is the loop this server exists for: your agent draws and maintains the diagram, and every doc that embeds it stays current.

![Customer 360 Platform](https://datadef.io/api/embed/customer-360-platform)

Connect

Two ways in — both included in the 7-day free trial:

• OAuth (no key): add `https://datadef.io/mcp` as a connector in Claude or ChatGPT and sign in when prompted. Claude Code works keyless too: `claude mcp add --transport http datadef https://datadef.io/mcp`.
• API key (headless): create one at datadef.io/settings/mcp and send it as a Bearer header — right for CI and scripts.

Claude Code

claude mcp add --transport http datadef https://datadef.io/mcp \
  --header "Authorization: Bearer dd_live_YOUR_KEY"

Cursor / Claude Desktop (via the `mcp-remote` bridge)

{
  "mcpServers": {
    "datadef": {
      "command": "npx",
      "args": [
        "-y", "mcp-remote", "https://datadef.io/mcp",
        "--header", "Authorization: Bearer dd_live_YOUR_KEY"
      ]
    }
  }
}

VS Code (`.vscode/mcp.json`)

{
  "servers": {
    "datadef": {
      "type": "http",
      "url": "https://datadef.io/mcp",
      "headers": { "Authorization": "Bearer ${input:datadef-key}" }
    }
  }
}

Gemini CLI (`~/.gemini/settings.json`) — note `httpUrl`, not `url`; plain `url` is legacy SSE there and fails silently:

{
  "mcpServers": {
    "datadef": {
      "httpUrl": "https://datadef.io/mcp",
      "headers": { "Authorization": "Bearer $DATADEF_API_KEY" }
    }
  }
}

One-click install buttons live at datadef.io/settings/mcp.

Tools

Three layers. Outcome-level — describe an intent, Datadef's pipeline carries it out: `create_diagram`, `list_diagrams`, `get_diagram`, `edit_diagram`, `export_diagram`, `get_design_guide`. Repository sync — `repo_status` and `repo_refresh` inspect and re-run the sync that keeps a diagram and its architecture.md regenerated from a connected GitHub/GitLab/Azure DevOps branch or tag. Terraform repositories get a dedicated pipeline: every `.tf` file parsed (no init, no state, no cloud credentials), modules drawn as zones, per-environment counts kept honest. Atomic — 25 `canvas_*` tools your model drives directly (add/update/remove nodes, connect edges, set columns, add lineage, group, align, layout, validate), so the agent that already knows your repo can draw what it finds. The `datadef_design_guide` prompt teaches any model the design standard before it draws.

Anonymous `initialize` and `tools/list` are open — point any MCP inspector at the endpoint to browse the surface before creating a key.

Why a diagram tool wants an agent

Run as a container

For clients or checkers that want a runnable image, the included Dockerfile

bridges stdio to the hosted server:

docker build -t datadef-mcp .
docker run -i -e DATADEF_API_KEY=dd_live_YOUR_KEY datadef-mcp

Links

• Try without an account: datadef.io/scratch
• MCP guide: datadef.io/guides/en/mcp-diagram-server
• Issues and feedback: right here.