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systembridge-mcp

by teyepe

systembridge-mcp: MCP Server for Design Systems

An AI-native Model Context Protocol (MCP) server that gives LLMs and agents deep knowledge of design systems and design tokens, enabling intelligent design system evolution, token analysis, and designer-to-developer handoffs.

License: MIT TypeScript MCP


What is this?

systembridge-mcp teaches AI assistants (like Claude, GPT, or custom agents) how to work with your design system. Instead of just answering questions about design, the AI can:

  • Understand your design tokens: browse, search, and explain color palettes, spacing scales, and typography

  • Plan new features: describe what you need ("a login page"), get components and tokens required

  • Audit designs before handoff: check if your design has all the tokens it needs, find gaps, verify accessibility

  • Match colors and components: upload a screenshot or describe UI, get back what matches your system

  • Generate and validate tokens: create palettes, check WCAG contrast, scaffold semantic tokens

  • Evolve your system: transform tokens, migrate naming, manage themes and brands

Designers: Think of it as a design system assistant that knows your token library. Ask "what red for error states?" or "what tokens should I apply on this mockup?" — no code required.

Developers: A programmatic interface to design tokens with AI-native tooling for analysis, transformation, and code generation.


Related MCP server: tokensStudioMCP

Quick Start

What you need: Node.js 18+ and an AI assistant that supports MCP (e.g. Claude Desktop, Cursor).

  1. Install in your design-token project:

    npm install -D systembridge-mcp
  2. Add a config file in your project root (optional; sensible defaults apply):

    // .systembridge-mcp.json or systembridge-mcp.config.json
    { "tokenPaths": ["tokens/**/*.json", "design-tokens/**/*.json"] }
  3. Add to your MCP config (e.g. .cursor/mcp.json for Cursor):

    {
      "mcpServers": {
        "systembridge-mcp": {
          "command": "npx",
          "args": ["systembridge-mcp"]
        }
      }
    }
  4. Restart your AI assistant. Look for the MCP icon — you should see 32 tools from systembridge-mcp. No path configuration needed when using project-level config.

Next: Getting Started | Setup by Client (Cursor, VS Code, Claude) | Agent Instructions (optional AI guidance).
For local development: TESTING.


Example Conversations

For Designers

"I need to design a dashboard with data tables and stat cards. What components and tokens do I need?"

The AI uses plan_flow to identify UI patterns, list required components, and tell you what token slots to fill.

"I'm using button, text-input, card, and modal. Does my design system cover everything?"

The AI uses audit_design to check for missing tokens, naming issues, and accessibility problems.

"I see a UI with colors #3B82F6, #EF4444, #F3F4F6. Which tokens match?"

The AI uses analyze_ui with perceptual color matching to find the closest tokens.

For Developers

"Generate a brand color palette with hue 220, chroma 0.7, and map it to semantic tokens for light mode."

The AI uses generate_palette + map_palette_to_semantics + check_contrast to create accessible tokens.

"Run a full audit of my design tokens. Check naming, accessibility, and coverage."

The AI uses audit_semantics + analyze_coverage + check_contrast to produce a health report.

"I have legacy tokens that don't follow our naming convention. Help me migrate them safely."

The AI uses analyze_topologygenerate_refactor_scenariosexecute_migration to plan and execute the migration.


Features

Area

Tools

Designer intelligence

plan_flow, audit_design, analyze_ui

Token management

search_tokens, validate_tokens, transform_tokens

Semantic tokens

scaffold_semantics, audit_semantics, analyze_coverage, describe_ontology

Color & accessibility

generate_palette, map_palette_to_semantics, check_contrast

Themes & brands

list_brands, resolve_brand, list_themes, resolve_theme, diff_brands, diff_themes

Scales

analyze_scales, generate_scale, suggest_scale, derive_density_mode, audit_scale_compliance, generate_fluid_scale

Migration

analyze_topology, audit_figma_usage, generate_refactor_scenarios, execute_migration

Figma

extract_figma_tokens, validate_figma_tokens, generate_component_docs

7 pre-built prompts orchestrate multiple tools: create-token, audit-tokens, design-semantic-tokens, design-color-palette, design-from-scratch, design-handoff-review, component-reference.

Full reference: docs/features.md


Documentation

Guide

Description

Getting Started

Full installation, config options, troubleshooting

Setup by Client

Per-client setup (Cursor, VS Code, Claude Desktop, Claude Code)

Agent Instructions

Optional AI usage guidance and rule templates

Local Testing

Test with npm link without absolute paths

For Designers

Designer workflow, example prompts, no-code usage

For Developers

CLI, MCP API, extending the server, tests, benchmarks

Features

Full feature and tool reference

Configuration

Config file, env vars, token paths

Migration System

Risk assessment and scenario generation

Migration Executor

Execution, validation, and rollback

Figma Integration

Figma variable sync and validation

AGENT_HANDOFF

Architecture guide for LLMs and agents


Contributing

Contributions welcome. Fork, create a feature branch, make your changes, run npm run build, and submit a pull request.

See docs/AGENT_HANDOFF.md for codebase architecture.


License

MIT — see LICENSE. Copyright © 2024–2026 Tasos Dervenagas.


Acknowledgments

Built on Model Context Protocol, Style Dictionary, Leonardo, and WCAG 2.1. Inspired by W3C DTCG, Tokens Studio, and design systems from Primer, Polaris, and Carbon.


Support: GitHub Issues · GitHub Discussions · MCP Discord

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