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figma-context-mcp-cached

Cache Figma design data locally to serve repeated MCP requests.

3|Updated May 16, 2026
Search Tags:#caching#figma#mcp#image export#design-to-code#component extraction#API rate limits
One-click install
npx skills add https://github.com/Aradotso/design-skills --skill figma-context-mcp-cached
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: figma-context-mcp-cached
Source: https://github.com/Aradotso/design-skills/tree/main/skills/figma-context-mcp-cached
Command: npx skills add https://github.com/Aradotso/design-skills --skill figma-context-mcp-cached

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents slow, rate-limited repeated access to Figma design files by caching downloaded design data locally for AI agents, enabling faster analysis cycles.

Core Features & Use Cases

  • Persistent disk caching with TTL to reduce Figma API calls during repeated design lookups.
  • MCP toolset for preparation, data retrieval, and asset export to support end-to-end workflows like extracting nodes/styles and downloading design images.
  • Force refresh and node validation to ensure agents can get the latest content after updates and handle nodeId-based requests reliably.
  • Use case: When iterating on a design system, repeatedly fetch the same Figma frames/components to extract colors/typography and generate UI code without re-downloading the entire file every time.

Quick Start

Set your Figma API key and configure the MCP client to start the figma-context-mcp-cached server, then ask your AI to run figma_prepare_file for the target Figma URL before calling get_figma_data.

Frequently Asked Questions about figma-context-mcp-cached

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I cache Figma design data to avoid API rate limits during AI code generation?▼

Caching Figma design data locally prevents slow, rate-limited repeated access by serving cached MCP outputs for repeated requests. This enables faster analysis cycles for AI agents automating design-to-code workflows without hitting Figma API rate limits.

What's the best way to extract Figma component structure and global styles for an AI agent?▼

Extracting Figma component structure and global styles requires running figma_prepare_file for the target Figma URL, then calling get_figma_data to retrieve the frame and node information. This MCP toolset supports reading design variables and downloading SVG/PNG assets for automation.

Do I need a Figma API key to use a cached MCP server for design file retrieval?▼

A Figma API key is required to use a cached MCP server for design file retrieval. You must set your Figma API key and configure the MCP client to start the server before asking your AI agent to run the preparation and data retrieval functions.

How do I download Figma images and SVG assets for specific design nodes?▼

Downloading Figma images and SVG assets is done by calling download_figma_images with optional nodeId parameters after preparing the file. The MCP toolset handles asset export with TTL-aware refresh behavior to ensure reliable node validation and current content retrieval.

How does TTL-aware refresh behavior work when fetching updated Figma frames?▼

TTL-aware refresh behavior controls when cached Figma design data expires and requires re-downloading from the API. Force refresh options ensure agents retrieve the latest content after design updates while persistent disk caching minimizes redundant API calls during repeated lookups.

Why does fetching Figma files repeatedly slow down my design system iteration workflow?▼

Fetching Figma files repeatedly slows down workflows because each request accesses the Figma API directly, encountering rate limits and latency. Persistent disk caching with TTL reduces these API calls by serving locally stored design data for repeated frame and component lookups.

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