tidy-core
Provides governance and health monitoring for Figma design systems, offering tools to assess adoption, drift, impact, and decision history rather than authoring components.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@tidy-coreshow adoption trends for the Button component"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
tidy-core
Other Figma MCP servers let an agent change your design system. tidy-core tells you whether it should, what will break, whether anyone adopted the last change, and what you decided six months ago.
Design system governance over the Model Context Protocol. Thirteen tools, not a hundred.
Status: v0.2.0, early. Five of the thirteen tools are implemented, including the plan/apply pair that makes changes reviewable. The other eight are registered and will tell you they are not built yet rather than returning an empty result. See the build sequence.
Why this exists
Reading and writing Figma from an agent is a solved problem. Figma ships a first-party MCP server, and there are good third-party ones with a hundred-plus tools.
What none of them do is answer the question a design system lead actually gets asked: is this working, and is it worth the headcount?
You can show a component count. You cannot show adoption, drift, decision history, or the cost of the next migration. So the system gets judged on output volume, which is the one metric that rewards the wrong behaviour.
tidy-core is built for that gap. It is not a faster way to draw components.
Related MCP server: Harness Engineering MCP
What it does
Capability | The question it answers |
Decision record | Why is the system like this? Which decisions now contradict each other? |
Longitudinal health | Is it improving or decaying, and which category moved? |
Adoption evidence | Do designers use the components, or detach and override them? |
Blast radius | What breaks if I rename this token, and what will the migration cost? |
Drift and parity | Does the design still match code, Storybook, and the docs? |
CI enforcement | Does this change violate a recorded decision? Block it. |
What it does not do
Not a design-to-code generator. Figma's own MCP server does that well.
Not a way to author components faster. Other servers do that well.
Not a tool-count competition. The small surface is the point.
It is designed to sit alongside an authoring MCP server, not replace one. Use those to make the change. Use this to decide whether to make it, and to prove what happened.
Install
👋 New to this?
Read the step-by-step guide instead →
It assumes no coding experience, explains every term, and tells you what should happen after each step. About 20 minutes.
The short version, for people who have set up an MCP server before.
Requires Node 18+ and Figma Desktop. A browser tab cannot reach a localhost WebSocket, and there is no workaround.
Not on npm yet, so clone and build:
git clone https://github.com/rominak/tidy-core.git
cd tidy-core
npm install
npm run buildClaude Code
claude mcp add tidy-core -s user -- node /absolute/path/to/tidy-core/dist/index.jsCursor, Windsurf, Claude Desktop
{
"mcpServers": {
"tidy-core": {
"command": "node",
"args": ["/absolute/path/to/tidy-core/dist/index.js"]
}
}
}Then connect the plugin:
Figma Desktop → Plugins → Development → Import plugin from manifest
Select
plugin/manifest.jsonfrom this repoRun it. It scans ports 9240 to 9249 and connects on its own.
Verify:
Check tidy statusFirst run
Load the design system contract.tidy_context returns collections, modes, token names grouped by intent, component variant axes, and the naming conventions actually in use, inferred from your data rather than asserted.
More in docs/examples.md.
Design decisions worth knowing
It refuses to guess which file you mean. With two or more Figma files connected and no target set, commands fail with a list of the files instead of picking the first open socket. Picking is a coin flip, and losing it writes to the wrong file. Set one with tidy_target.
It tags every response. Real setups run several Figma MCP servers side by side. Every response carries _mcp: "tidy-core" and every error is prefixed [tidy-core], so another server's failure is never blamed on this one.
Unbuilt tools say so. The eight planned tools are registered and return an explicit refusal naming what they will do. A tool that silently returns {} is worse than one that admits it does not exist yet.
It will not kill your other tools. If port 9240 is busy it moves to the next free one in 9240 to 9249, which the plugin scans anyway. It deliberately does not terminate whatever was holding the port.
Reads will write. Once tidy_health and tidy_adoption land, every call persists a snapshot. That is what makes the second run able to show a delta without anyone scheduling anything. A noCapture flag will exist for read-only CI checks. See the reasoning.
The thirteen tools
Tool | Status | Question |
| ✅ | Where am I connected, and where will the next command land? |
| ✅ | Which file am I working in? |
| ✅ | What is in this system, so I can work correctly? |
| 🚧 | How healthy is it, and which direction is it moving? |
| 🚧 | Is anyone actually using it? |
| 🚧 | Does design still match code, Storybook, docs? |
| 🚧 | What breaks if I change this? |
| 🚧 | What should I fix first? |
| ✅ | Give me a safe, reviewable sequence of changes. |
| ✅ | Execute the plan I reviewed. |
| 🚧 | Why is it like this, and do our decisions contradict? |
| 🚧 | Capture why we did this. |
| 🚧 | Should this change be allowed to merge? |
Full definitions in docs/spec.md.
Development
npm install
npm run build # emits dist/, excludes tests
npm test
npm run typecheck # checks everything including tests
npm run dev # watch modeEverything runs locally. No data leaves your machine, and there is no telemetry.
Learn more
tidy-core comes out of ongoing work on design systems and AI at aidesign.guide, where the thinking behind it is written up in more depth: how design systems get measured, where AI actually helps, and where it quietly makes things worse.
Built by Romina Kavčič.
License
MIT
Maintenance
Resources
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