The Zeplin MCP server acts as a professional bridge between design files and coding environments, allowing AI coding assistants to "read" designs just as a human developer would. Instead of requiring a developer to manually describe a user interface to an AI, this tool gives models direct access to the source of truth within Zeplin projects. It simplifies the development process by enabling AI agents to understand exactly how a visual element should look and behave before a single line of code is written. Moving into more technical territory, the server provides high-fidelity data including precise component specifications, screen layouts, and production-ready assets. It specifically pulls in documentation and annotations, meaning the AI isn't just guessing based on a static image; it understands the intended logic, interaction patterns, and implementation notes provided by the design team. This depth of information allows AI agents to generate front-end code that matches design requirements with a level of accuracy that vision-only models often struggle to achieve. For developers working within established design systems, the tool offers the advanced capability to tap into design tokens, such as standardized colors, typography, and spacing variables. This ensures that the AI generates code consistent with the project's existing CSS or theme configuration. Furthermore, the server includes intelligent controls to manage the AI's context window, such as the ability to focus on specific layers or exclude unnecessary screen variants, making it an efficient way to build complex, enterprise-grade interfaces through iterative, AI-assisted workflows.
Category: Design, Media & Creative
Tags: css, design-tokens, frontend, ui-design, zeplin
bash npx @zeplin/mcp-server@latest<YOUR_ZEPLIN_PERSONAL_ACCESS_TOKEN> with your actual token. jsonc { "mcpServers": { "zeplin": { "command": "npx", "args": ["@zeplin/mcp-server@latest"], "env": { "ZEPLIN_ACCESS_TOKEN": "<YOUR_ZEPLIN_PERSONAL_ACCESS_TOKEN>" } } } }get_screen: Fetches detailed specs and assets for entire screens. * Parameters: * includeVariants (boolean): Set to false to fetch only the specific screen version linked rather than all states/sizes. * targetLayerName (string): Filters the response to return data primarily for a specific layer and its children. * get_component: Fetches specs and assets for specific components. * Design Token Access: Fetches colors, typography, spacing, and other design variables. * Documentation Access: Fetches annotations and context added to screens for behavioral instructions.https://zpl.io/abc123X. I only need the specific version linked, not all its variants." (This prompts the agent to use includeVariants: false)./zpl.io/screenXYZ`) and prompts: "Implement the landing page hero section from this design using Tailwind CSS and React." The AI fetches the specs via the MCP and writes the component with the exact spacing and colors defined by the designer.
color: #3b82f6) instead of using standardized design tokens (e.g., color: var(--primary-blue)). This makes global design updates nearly impossible to manage. Solution: The Zeplin MCP allows the AI to tap into the project’s design tokens (colors, typography, and spacing variables). The AI can be instructed to prioritize these existing tokens when generating or refactoring code, ensuring the implementation remains consistent with the design system. Example: A developer prompts the AI: "Refactor the Button.tsx component. Use the Zeplin MCP to find the correct design tokens for our primary and secondary states and replace all hardcoded hex values with the corresponding project variables."targetLayerName) or exclude variants. This allows the AI to focus its "attention" only on the specific part of the design that changed, reducing errors and saving token usage. Example: A developer uses the prompt: "The design for the 'Header' has changed. Focus on the 'NotificationIcon' layer in this Zeplin screen: `https:/zpl.io/abc123X`. Update the component to include the new red dot indicator as shown in the specs."
Part of MCP Servers
The Zeplin MCP server is an official Model Context Protocol integration that exposes Zeplin designs, screens, components, and design tokens to AI coding assistants. It allows AI models to inspect design specifications and generate accurate front-end code based on documented specifications.
The Zeplin MCP server works with MCP-compatible clients and AI editors including Cursor, VS Code with Copilot or MCP extensions, Windsurf, and Claude Code. Any client supporting NPX command execution and environment variables can connect to the server.
The server provides tools such as get_screen to fetch screen layouts, specifications, and assets, and get_component to retrieve individual component details. It also supports parameters like includeVariants to exclude excess states and targetLayerName to focus data queries on specific layers.
You can manage context consumption by setting the includeVariants parameter to false on get_screen calls, by using targetLayerName to fetch only specific child layers, or by breaking screen implementations into small, component-first steps.
You can install it by configuring your MCP client to run npx @zeplin/mcp-server@latest with Node.js v20 or later. You must also supply your Zeplin personal access token via the ZEPLIN_ACCESS_TOKEN environment variable.