Figma to Flutter MCP Server

The Figma to Flutter MCP Server bridges the gap between design and code by exposing Figma’s rich file metadata through the Model Context Protocol. Instead of manually translating a design into Flutter widgets, an AI assistant can query this server to retrieve structured information about screens, components, layout hierarchies, and styling. This eliminates repetitive manual work and ensures that the generated Flutter UI stays faithful to the original design intent.

For developers building AI‑powered coding agents, the server is a powerful asset. It offers node extraction that returns layout, color, typography, and hierarchy data for every element in a Figma file. It also supplies screen metadata such as device type, orientation, and navigation structure, enabling the assistant to propose appropriate Flutter configurations. By providing this context instead of raw code, the server empowers AI models to produce cleaner, more maintainable Flutter code that follows best practices and aligns with the design system.

Key capabilities include:

• Component analysis: Detects nested components, visual importance, and suggests suitable Flutter widgets (e.g., , , ).
• Screen mapping: Identifies headers, footers, navigation bars, and content areas to guide scaffold creation.
• Design consistency: Supplies color palettes, text styles, and spacing values directly from Figma, ensuring that the Flutter UI inherits the exact visual language.
• Non‑code output: The server focuses on data extraction rather than code generation, allowing developers to maintain control over the final implementation while still leveraging AI for boilerplate and structure.

Typical use cases involve a workflow where a UI designer creates or updates a Figma file, an AI assistant queries the MCP server for updated component and screen data, and then generates or refactors Flutter code snippets that match the new design. This is especially valuable in continuous integration pipelines, rapid prototyping, or when onboarding new developers who need to understand the design system quickly.

Integrating the server into AI workflows is straightforward: an assistant simply sends a structured query over MCP, receives JSON‑encoded design metadata, and uses that information to inform code generation prompts. Because the server operates over a standard protocol, it can be combined with any AI platform that supports MCP—whether it’s Cursor, Claude, or a custom in‑house agent—making it a versatile addition to modern development toolchains.