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Give AI assistants structured access to Open edX Paragon component docs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Paragon MCP Server" yet — see the docs or source repo.
Find the props, supported events, and the most basic usage example for a Paragon form component.
Returns the component's props, event details, and a concise code example for quick integration.
Provide example code for a Paragon button or modal component and explain suitable usage scenarios.
Outputs reusable component sample code with brief scenario guidance.
List Paragon CSS design tokens related to color or spacing and explain what each is used for.
Returns relevant design tokens and their purposes to help keep UI styling consistent.
When building Open edX-related interfaces, developers can use this MCP server to let an AI assistant retrieve component props, events, and example code. It is useful for quickly locating documentation details without manual browsing.
Designers or developers can use the tool to inspect Paragon CSS design tokens for consistent colors, spacing, and other UI standards. This helps reduce drift between implementation and the design system.
During collaboration with design and engineering, product managers can ask an AI assistant to summarize what a component supports and show examples. This helps assess whether a proposed UI fits the existing design system.
It is an MCP server for the Open edX Paragon design system. It gives AI assistants structured access to component documentation, including props, events, code examples, and CSS design tokens.
Based on the description, it can provide structured component documentation, including props, events, code examples, and CSS design tokens. For more details, see the source repository.
The provided materials do not include installation steps, runtime requirements, or key requirements. See the source repository for those details.
Let AI agents securely use prebuilt actions across 130+ SaaS apps.
Lets AI access design tokens and component contracts via MCP consistently.
Gives AI coding agents queryable design system guidance for accurate implementation.
Offload bulk classification, extraction, and summarization to distributed open-source models.
Search, read, and discover related design system components via MCP.
Discover design system components and generate implementation-ready frontend code automatically.