The paper introduces the beta release of Figma’s Model Context Protocol (MCP) server, designed to integrate Figma directly into the developer workflow to enable Large Language Models (LLMs) to achieve design-informed code generation. Traditionally, providing design context to AI tools was limited to feeding images or API responses to chatbots. The Figma MCP server leverages the Model Context Protocol (MCP), a new standard for applications to supply context to LLMs, to overcome this limitation.

The core methodology revolves around enhancing LLMs' ability to generate precise, informed, and system-aligned code by providing rich, contextual design information from Figma. This extends beyond the LLM’s training data and typical agentic tooling that provides codebase context (e.g., existing code, repository history, documentation, database schemas). The Figma MCP server adds the crucial "design fingerprint" to ensure generated code aligns not just with the codebase's unique structure, framework, vocabulary, and workflow, but also with the design intent.

The server translates design intent for LLMs by mimicking a human developer's holistic understanding process, from high-level overviews to intricate details. It achieves this by surfacing specific types of context, allowing configuration to control what information is returned, through four primary mechanisms:

1. Pattern Metadata: This mechanism leverages existing design system investments, such as components, variables, and styles that are already aligned between design and code. By providing direct references to these patterns, the server significantly improves the precision and efficiency of generated code while reducing LLM token usage. For instance, instead of an LLM inferring a component from a screenshot and potentially searching extensively or creating a new one, the Figma MCP server, via Code Connect, can provide the exact file path to the corresponding code component. Similarly, for design tokens (variables), it can supply the precise variable name and its associated code syntax (e.g., CSS variable name for a color), rather than just a hexadecimal value, enabling the LLM to use the correct design system primitive.

2. Screenshots: While pattern metadata is crucial for system alignment, visual context remains vital. Screenshots are provided to convey interactive content representation (e.g., an image of a map to represent an embedded map feature) or high-level structural understanding. These high-level screenshots capture the overall flow, sequence of screens, responsive contexts (mobile/desktop), and relationships between design sections or nodes, providing the LLM with an understanding of design intent that goes beyond style details. This visual information is supplementary, and when combined with Figma’s code outputs, it yields superior performance in code generation compared to either method alone.

3. Interactivity (Pseudocode): To describe design behaviors and functionality, the server provides pseudocode or code examples. This "code prototype" is more effective than simply enumerating properties, especially when informed by the codebase through features like code syntax for variables and Code Connect for components. This approach is particularly useful for representing encapsulated functionality (e.g., a stateful component) or highlighting differences in a sequence of UI states. An example cited is the server providing working React and Tailwind CSS representations of an image gallery, which LLMs can directly incorporate.

4. Content: The server also extracts and provides "placeholder content" from Figma, such as text strings, SVG assets, images, layer names, and annotations. This content is crucial for LLMs to derive how to populate the generated interface with actual data models from the code side. By understanding the implied content and its structure (e.g., through layer names or specific text elements), the LLM can more accurately infer the data relationships required for the final code.

The Figma MCP server is currently in beta, offering three primary tools to retrieve context: one for code, one for images, and one for variable definitions, accessible for current selections or specific node IDs. The code tool's response type is configurable (e.g., React and Tailwind output). Future developments include remote server capabilities, deeper codebase integrations, and support for features like annotations and Grid systems.