Design System MCP Server

Overview

The Design System MCP Server bridges the gap between AI assistants and structured design knowledge. It exposes Appian’s Aurora design system documentation—both public and private—to language models via the Model Context Protocol. By doing so, it allows developers to ask questions about components, layouts, patterns, and branding directly from an AI interface, receiving accurate, up‑to‑date guidance without leaving their development environment.

What Problem Does It Solve?

Design systems are living assets that evolve as teams iterate on UI patterns and component libraries. Traditional access requires navigating GitHub repositories or browsing a static web site, which can be cumbersome when troubleshooting or onboarding. The MCP server eliminates this friction by presenting the documentation as a first‑class data source that AI assistants can query in natural language. This streamlines knowledge discovery, reduces context switching, and ensures that developers always reference the latest internal or public content.

Core Functionality and Value

At its heart, the server offers a searchable catalog of design system artifacts. It parses repository files into structured categories—components, layouts, patterns, branding—and makes them available through MCP endpoints. Key benefits include:

• Unified Access: One query can span both public and internal repositories, with priority rules that let private updates override public documentation when needed.
• Source Transparency: Every result carries metadata indicating whether it originates from the public or internal repo, enabling developers to trace back to the original source.
• Dynamic Refresh: Administrators can trigger content refreshes, ensuring that new commits are reflected in AI responses without manual intervention.

Key Features Explained

• Multi‑source support: Simultaneous access to public GitHub repos and optional private repositories, configured via environment variables.
• Priority‑based merging: When a component exists in both public and internal sources, the internal version takes precedence, allowing teams to maintain custom overrides.
• Browse & list: AI clients can request a list of all categories or the components within a category, complete with source tags.
• Detailed component lookup: Retrieval of full documentation—including guidance text and code snippets—for a specific component.
• Keyword search: Full‑text search across all components, with the ability to filter results by source.
• Source management: Endpoints for inspecting repository status and manually refreshing cached content.

Real‑World Use Cases

• Onboarding new developers: A newcomer can ask an AI assistant, “What are the available button components?” and receive a curated list with examples, eliminating the need to sift through markdown files.
• Design review assistance: During a code review, an engineer can request, “Show me the latest layout guidelines for dashboards,” and instantly get the relevant documentation.
• Continuous integration: CI pipelines can invoke the MCP to verify that component usage in code matches documented patterns, feeding results back into a conversational tool for quick remediation.
• Product documentation: Technical writers can query the MCP to pull consistent component descriptions into product manuals or knowledge bases.

Integration with AI Workflows

Developers can configure the server in their MCP client (e.g., Amazon Q, Claude Desktop) by adding a reference to the server’s JSON descriptor. Once connected, AI assistants can call specific endpoints—such as “list components in the category” or “search for ”—and receive structured responses. The server’s source attribution allows the assistant to transparently inform users whether a piece of information comes from an internal private repo or the public Aurora documentation, fostering trust and clarity.

Unique Advantages

• Seamless dual‑source management: Unlike generic documentation APIs, this server natively supports both public and private repositories with a clear override strategy.
• Designed for design systems: The category layout mirrors common design system structures, making it intuitive for UI/UX teams to map their documentation into the server.
• Built‑in refresh mechanism: Automatic or manual cache invalidation ensures that AI responses reflect the latest codebase changes without downtime.

In summary, the Design System MCP Server empowers developers to harness the full power of conversational AI for design system exploration, ensuring that accurate, up‑to‑date component knowledge is always at their fingertips.