Use Cases & Scenarios

This guide shows real-world scenarios for using Figma Console MCP in your workflow.

🐛 Plugin Development & Debugging

Scenario 1: Simple Plugin Debugging (Local Mode - Easiest!)

Your situation: You’re developing a Figma plugin and want to see console output. One-time setup: Connect to Figma Desktop using one of these methods:

Install the Desktop Bridge Plugin (Plugins → Development → Import from manifest) and run it in your file

Then open your design file and run your plugin. What to say to your AI assistant:

"Check the last 20 console logs"

Then run your plugin in Figma Desktop, and say:

"Check the last 20 console logs again"

What happens:

AI retrieves current console logs (likely empty initially)
You run your plugin in Figma Desktop
AI retrieves logs again - now showing ALL plugin output: [Main], [Swapper], [Serializer], etc.
You see errors, warnings, and log statements with timestamps

Follow-up prompts:

“Show me just the error logs”
“What does this stack trace mean?”
“Help me fix this error”

Why this works: In local mode, the MCP automatically monitors Figma Desktop. No navigation needed!

Scenario 2: Debug Console Errors in Plugin (Cloud Mode)

Your situation: You’re using cloud mode or need to debug a specific Figma file URL. What to say to your AI assistant:

"Navigate to my Figma file at https://figma.com/design/abc123 and watch console logs for 30 seconds while I test my plugin"

What happens:

AI navigates to your Figma file
Starts monitoring console logs in real-time
Captures any errors, warnings, or log statements
Reports back with timestamped logs and stack traces

Follow-up prompts:

“Show me just the error logs”
“What does this stack trace mean?”
“Help me fix this error”

Scenario 3: Monitor Plugin Performance

Your situation: You want to see what your plugin is logging during execution. What to say:

"Navigate to https://figma.com/design/abc123 and watch console for 60 seconds. Show me all console.log statements"

What happens:

AI monitors all console output for 60 seconds
Captures every console.log(), console.info(), console.warn()
Shows you a timeline of what your plugin is doing

Scenario 4: Debug Plugin with Screenshots

Your situation: Plugin UI isn’t rendering correctly. What to say:

"Navigate to my plugin file, take a screenshot of the plugin UI, then show me console errors"

What happens:

AI navigates to your file
Takes screenshot showing the current state
Retrieves console errors
You can see both visual state and error logs together

🎨 Design System Extraction

Scenario 4b: Extract Full Design System for AI Code Generation

Your situation: You want to feed your design system to an AI code generator (Lovable, v0, Replit, Cursor) so it builds with your actual tokens, components, and styles instead of generic defaults. What to say:

"Use figma_get_design_system_kit to extract my full design system from file key abc123, then use those tokens and component specs to build a signup page"

What happens:

AI calls figma_get_design_system_kit — gets tokens, components, and styles in one call
Receives visual specs (exact colors, padding, typography, layout) for each component
Uses actual token values (light/dark modes) instead of hardcoded values
Builds the page using your real design system variables

Variations:

“Extract only my tokens and styles” → include: ["tokens", "styles"]
“Get my Button and Card components with images” → componentIds: ["1:234", "5:678"] + includeImages: true
“Just get an inventory of what’s in my design system” → format: "compact"

Why use this instead of individual tools: One call replaces figma_get_variables + figma_get_component + figma_get_styles, keeps the AI context window clean, and includes visual specs that individual tools don’t provide.

Scenario 5: Extract Design Tokens

Your situation: You need to extract all design variables from your Figma design system. What to say:

"Get all design variables from https://figma.com/design/abc123 and export them as CSS custom properties"

What happens:

AI extracts all variables using Figma API
Formats them as CSS custom properties
Provides organized, ready-to-use CSS code

Example output:

:root {
  /* Colors */
  --color-primary-default: #4375FF;
  --color-primary-hover: #2563EB;

  /* Spacing */
  --spacing-xs: 4px;
  --spacing-sm: 8px;

  /* Typography */
  --font-size-body: 16px;
  --line-height-body: 24px;
}

Scenario 6: Generate Tailwind Config

Your situation: You want to sync your Figma variables to Tailwind CSS. What to say:

"Get variables from https://figma.com/design/abc123 and export as Tailwind config"

What happens:

AI extracts variables
Converts to Tailwind format
Provides tailwind.config.js code

Scenario 7: Audit Design System Usage

Your situation: You want to see which components are using specific design tokens. What to say:

"Get all variables from my design system and show me where each one is used"

What happens:

AI extracts variables with enrichment enabled
Shows usage analysis
Lists which styles/components use each variable

🔧 Component Implementation

Scenario 8: Implement Component from Figma

Your situation: You need to implement a Tooltip component from your design file. What to say:

"Get the Tooltip component from https://figma.com/design/abc123?node-id=695-313 and help me implement it in React"

What happens:

AI fetches component data with visual reference image
Extracts layout, styling, and property information
Helps you implement with accurate spacing, colors, and behavior

AI will provide:

Component image for visual reference
Layout properties (padding, spacing, auto-layout)
Color and typography specs
Implementation guidance

Scenario 9: Get Component Specifications

Your situation: You just need the specs for a component, not implementation help. What to say:

"Get visual reference and layout specs for the Button component at node-id=123:456"

What happens:

AI renders component as high-res image
Extracts layout measurements
Lists color values and typography
You implement it yourself with accurate specs

Scenario 10: Compare Multiple Component Variants

Your situation: You have a Button component with Primary, Secondary, and Tertiary variants. What to say:

"Get component data for these three button variants: node-id=1:2, node-id=1:3, node-id=1:4. Show me the differences"

What happens:

AI fetches all three variants
Compares their properties
Highlights what changes between variants (colors, borders, padding, etc.)

✏️ Design Creation (Local Mode & Cloud Mode)

These scenarios require the Desktop Bridge plugin. In Local Mode, the plugin connects via WebSocket. In Cloud Mode, say “connect to my Figma plugin” first to pair via the cloud relay — then all the same prompts work from web-based AI clients like Claude.ai, v0, Replit, and Lovable.

Scenario 11: Create Component Variants with Variables

Your situation: You want to create a complete button component set with multiple variants, sizes, and states—all bound to design system variables. Prerequisites:

Local mode with Desktop Bridge plugin running
Design system variables defined (colors, spacing, radius)

What to say:

"Create a button component with:
- 4 types: primary, secondary, outline, ghost
- 3 sizes: small, medium, large
- 5 states: default, hover, active, focus, disabled

Bind the colors to my design system variables (piccolo for primary, beerus for secondary, etc.) and use the spacing/radius variables for padding and border-radius."

What happens:

AI loads required fonts
Creates individual component variants (60 total: 4×3×5)
Applies variable bindings for colors, padding, and border-radius
Combines into a component set
Calls figma_arrange_component_set to organize with labels and proper layout
Takes a screenshot to verify the result

Follow-up prompts:

“Add descriptions to each variant”
“Adjust the hover states to use darker colors”
“Create a similar set for icon buttons”

Scenario 12: Build a Design System from Scratch

Your situation: You’re starting a new project and need to set up design system foundations. What to say:

"Create a design system with:
1. A color variable collection with:
   - Primary colors (piccolo, beerus, goten)
   - Neutral colors (bulma, trunks, goku)
   - Semantic colors (success, warning, error)
2. A spacing variable collection with xs (4px) through 2xl (48px)
3. A radius variable collection with none, sm, md, lg, full

Then create a basic button component using these variables."

What happens:

AI creates variable collections
Adds variables with appropriate values
Creates a button component bound to the variables
Provides a screenshot of the result

Scenario 13: Organize Existing Component Set

Your situation: You have a messy component set that needs proper organization with labels. What to say:

"Find my 'Button' component set on the current page and arrange it with:
- Proper variant labels
- Purple dashed border container
- Full-width frame"

What happens:

AI finds the component set by name
Calls figma_arrange_component_set with your specifications
Variants are organized in a grid with row/column labels
Everything is wrapped in a properly styled container

Scenario 14: Create Component with Auto-Layout

Your situation: You need a card component with proper auto-layout settings. What to say:

"Create a card component with:
- Vertical auto-layout, 16px gap
- 24px padding all around
- Rounded corners (12px)
- Contains: image placeholder, title text, description text, action button
- Bind padding to my spacing variables"

What happens:

AI executes Plugin API code to create the frame
Sets up auto-layout with specified settings
Creates child elements (image, text, button)
Binds padding to design system variables
Converts to component

Scenario 15: Add Documentation to Components

Your situation: You want to add descriptions to your components for design system documentation. What to say:

"Add a description to my Button component explaining:
- When to use each variant
- Accessibility requirements
- Usage guidelines

Use markdown formatting."

What happens:

AI finds the Button component
Calls figma_set_description with markdown-formatted documentation
Description appears in Figma’s component panel

Scenario 16: Clone and Modify Existing Design

Your situation: You want to create variations of an existing component. What to say:

"Clone my 'Card/Default' component 3 times and modify each:
'Card/Compact' - reduce padding to 12px
'Card/Featured' - add a colored top border
'Card/Minimal' - remove the image placeholder"

What happens:

AI clones the component using figma_clone_node
Modifies each clone using node manipulation tools
Renames using figma_rename_node
Takes a screenshot to show all variations

Scenario 17: Variable Mode Management (Local Mode & Cloud Mode)

Your situation: You need to add a dark mode to your design system variables. This works in both Local Mode and Cloud Mode (after pairing). What to say:

"Add a 'Dark' mode to my colors collection and set up the dark mode values:
- piccolo: #60A5FA (lighter blue)
- beerus: #A78BFA (lighter purple)
- background: #1F2937 (dark gray)
- text: #F9FAFB (light gray)"

What happens:

AI finds the colors collection
Adds a new mode using figma_add_mode
Updates each variable with dark mode values using figma_update_variable
Confirms the changes

Follow-up prompts:

“Show me all variables with their light and dark values”
“Create a preview frame showing both modes”

Scenario 18: Batch Component Creation

Your situation: You need to create a set of icon buttons with consistent styling. What to say:

"Create icon button components for these actions:
- Edit (pencil icon)
- Delete (trash icon)
- Copy (duplicate icon)
- Share (share icon)

Each should have 3 sizes (24px, 32px, 40px) and use the same variable bindings as my regular buttons."

What happens:

AI creates each icon button variant
Applies consistent styling and variable bindings
Combines into a component set
Organizes with figma_arrange_component_set
Takes a final screenshot

Scenario 19: Interactive Iteration Workflow

Your situation: You’re creating a component and want to refine it based on visual feedback. What to say:

"Create a navigation bar component with a logo, menu items, and a CTA button. After each step, show me a screenshot so I can give feedback."

What happens:

AI creates initial navigation structure → takes screenshot
You provide feedback (“make the menu items larger”)
AI modifies → takes screenshot
Continue iterating until you’re satisfied

This workflow leverages the screenshot feedback loop for precise design control.

☁️ Cloud Mode Workflows

These scenarios are for web-based AI clients (Claude.ai, v0, Replit, Lovable) that connect to Figma through the cloud relay. No local installation required — just the Desktop Bridge plugin running in Figma.

Scenario: Connect Cloud AI to Figma (First Time)

Your situation: You’re using Claude.ai (or v0, Replit, Lovable) and want to create or modify designs in Figma. What to say:

"Connect to my Figma plugin so we can start designing"

"Pair with my Figma file"

What happens:

AI generates a 6-character pairing code (valid for 5 minutes)
You enter the code in the Desktop Bridge plugin’s Cloud Mode section
The plugin connects to the cloud relay — you’re paired
All write tools (43 total) are now available through the cloud

Follow-up prompts:

“Create a card component with an image, title, and description”
“Update my primary color variable to #006699”
“Show me what components are in this file”

Scenario: Design System Extraction for AI Code Generators (Cloud)

Your situation: You’re using v0, Replit, or Lovable and want your actual design tokens — not REST API snapshots that require an Enterprise plan. What to say:

"Connect to my Figma file and extract my design system variables"

What happens:

AI generates a pairing code, you enter it in the plugin
Plugin API extracts ALL variables on any Figma plan (Pro, Org, or Enterprise)
AI receives full token data: colors, spacing, typography, with all mode values (Light/Dark/etc.)
You can now generate code using your real design system

Why this matters: The Figma REST API requires an Enterprise plan to access variables. Cloud Relay bypasses this limitation because the Desktop Bridge plugin uses the Plugin API, which works on all plans.

Scenario: Update Design Tokens from Web Chat

Your situation: You’re in a meeting, using Claude.ai on a laptop, and want to update a brand color across your design system. What to say:

"Connect to Figma and change the primary brand color to #006699 across all modes"

What happens:

AI generates pairing code, you enter it in the Desktop Bridge plugin
AI reads your current variables to find the primary color
Updates the value across Light, Dark, and any other modes
Confirms the changes with the new values

Scenario: Create Components from Web AI Client

Your situation: You’re using Claude.ai to prototype a new component and want it created directly in Figma. What to say:

"Connect to my Figma and create a notification toast with an icon, title text, and a dismiss button"

What happens:

AI generates pairing code, you enter it in the plugin
AI uses the plugin API to create a frame with auto-layout
Adds child elements (icon placeholder, title text, dismiss button)
Takes a screenshot to verify the result and show you what was created

🔍 Visual Debugging Workflows

Scenario 20: Document Plugin State

Your situation: You want to show someone what your plugin looks like at a specific point. What to say:

"Navigate to my plugin, take a full-page screenshot, and save it as 'plugin-error-state'"

What happens:

AI takes full-page screenshot
Saves with your custom filename
You can share the visual state with your team

Scenario 21: Monitor Visual Changes

Your situation: Testing if plugin UI updates correctly. What to say:

"Take a screenshot, then I'll make a change, then take another screenshot"

What happens:

AI takes “before” screenshot
You make your changes
AI takes “after” screenshot
You can compare the two states

🚀 Advanced Workflows

Scenario 22: Full Design System Export

Your situation: Migrating from Figma to code. What to say:

"Extract everything from my design system:
Get all variables and export as CSS
Get all text styles and export as Tailwind
Get all color styles as Sass variables
List all components"

What happens:

AI systematically extracts all design system data
Provides multiple export formats
Organizes everything for your codebase

Scenario 23: Plugin Development Sprint

Your situation: Rapid plugin development with continuous debugging. Workflow:

1. "Watch console for 5 minutes while I develop"
   → AI monitors in background

2. "Show me any errors from the last 2 minutes"
   → AI filters recent error logs

3. "Take a screenshot of current state"
   → Visual checkpoint

4. "Reload the plugin and clear console"
   → Fresh start

5. Repeat...

Scenario 24: Design Token Migration

Your situation: Moving from Figma Styles to Variables. What to say:

"Compare my old styles with new variables. Show me what changed and generate migration scripts"

What happens:

AI gets both styles and variables
Maps old → new
Identifies breaking changes
Suggests migration approach

💡 Tips for Effective Prompts

✅ Good Prompts

Be specific: “Get the primary button component from https://figma.com/design/abc123?node-id=1:2”
Include URL: Always provide your Figma file URL
State intent: “…and help me implement it in React” (tells AI what you’ll do with the data)
Request format: “export as CSS” vs “export as Tailwind”

❌ Avoid Vague Prompts

❌ “Get my design system” (which file?)
❌ “Help with my plugin” (what specifically?)
❌ “Show me components” (which ones? what data?)

🎯 Pro Tips

Chain operations: “Navigate to X, watch console for 30s, then screenshot”
Use filters: “Show me only error logs from the last minute”
Be specific about formats: “Export as Tailwind v4 syntax”
Request enrichment explicitly: “Get variables with CSS exports and usage information”
Cloud Mode — connect first: Start by saying “pair with my Figma plugin” or “connect to Figma” before asking for design changes
Use natural language: You don’t need to know tool names. Say “create a blue square” not “call figma_execute”

🔄 Integration with Other Tools

With Figma Official Dev Mode MCP

Workflow:

Use Figma Dev Mode MCP to generate component code
Use Figma Console MCP to get design token values
Replace hardcoded values with tokens
Use Console MCP to debug when integrated

Example:

// Step 1: Dev Mode MCP generates
<Button className="bg-[#4375ff]">Click me</Button>

// Step 2: Console MCP provides token
--color-primary: #4375FF

// Step 3: You refactor
<Button className="bg-primary">Click me</Button>

Getting Started

Guides

Reference

​Use Cases & Scenarios

​🐛 Plugin Development & Debugging

​Scenario 1: Simple Plugin Debugging (Local Mode - Easiest!)

​Scenario 2: Debug Console Errors in Plugin (Cloud Mode)

​Scenario 3: Monitor Plugin Performance

​Scenario 4: Debug Plugin with Screenshots

​🎨 Design System Extraction

​Scenario 4b: Extract Full Design System for AI Code Generation

​Scenario 5: Extract Design Tokens

​Scenario 6: Generate Tailwind Config

​Scenario 7: Audit Design System Usage

​🔧 Component Implementation

​Scenario 8: Implement Component from Figma

​Scenario 9: Get Component Specifications

​Scenario 10: Compare Multiple Component Variants

​✏️ Design Creation (Local Mode & Cloud Mode)

​Scenario 11: Create Component Variants with Variables

​Scenario 12: Build a Design System from Scratch

​Scenario 13: Organize Existing Component Set

​Scenario 14: Create Component with Auto-Layout

​Scenario 15: Add Documentation to Components

​Scenario 16: Clone and Modify Existing Design

​Scenario 17: Variable Mode Management (Local Mode & Cloud Mode)

​Scenario 18: Batch Component Creation

​Scenario 19: Interactive Iteration Workflow

​☁️ Cloud Mode Workflows

​Scenario: Connect Cloud AI to Figma (First Time)

​Scenario: Design System Extraction for AI Code Generators (Cloud)

​Scenario: Update Design Tokens from Web Chat

​Scenario: Create Components from Web AI Client

​🔍 Visual Debugging Workflows

​Scenario 20: Document Plugin State

​Scenario 21: Monitor Visual Changes

​🚀 Advanced Workflows

​Scenario 22: Full Design System Export

​Scenario 23: Plugin Development Sprint

​Scenario 24: Design Token Migration

​💡 Tips for Effective Prompts

​✅ Good Prompts

​❌ Avoid Vague Prompts

​🎯 Pro Tips

​🔄 Integration with Other Tools

​With Figma Official Dev Mode MCP

​📚 More Examples

Use Cases & Scenarios

🐛 Plugin Development & Debugging

Scenario 1: Simple Plugin Debugging (Local Mode - Easiest!)

Scenario 2: Debug Console Errors in Plugin (Cloud Mode)

Scenario 3: Monitor Plugin Performance

Scenario 4: Debug Plugin with Screenshots

🎨 Design System Extraction

Scenario 4b: Extract Full Design System for AI Code Generation

Scenario 5: Extract Design Tokens

Scenario 6: Generate Tailwind Config

Scenario 7: Audit Design System Usage

🔧 Component Implementation

Scenario 8: Implement Component from Figma

Scenario 9: Get Component Specifications

Scenario 10: Compare Multiple Component Variants

✏️ Design Creation (Local Mode & Cloud Mode)

Scenario 11: Create Component Variants with Variables

Scenario 12: Build a Design System from Scratch

Scenario 13: Organize Existing Component Set

Scenario 14: Create Component with Auto-Layout

Scenario 15: Add Documentation to Components

Scenario 16: Clone and Modify Existing Design

Scenario 17: Variable Mode Management (Local Mode & Cloud Mode)

Scenario 18: Batch Component Creation

Scenario 19: Interactive Iteration Workflow

☁️ Cloud Mode Workflows

Scenario: Connect Cloud AI to Figma (First Time)

Scenario: Design System Extraction for AI Code Generators (Cloud)

Scenario: Update Design Tokens from Web Chat

Scenario: Create Components from Web AI Client

🔍 Visual Debugging Workflows

Scenario 20: Document Plugin State

Scenario 21: Monitor Visual Changes

🚀 Advanced Workflows

Scenario 22: Full Design System Export

Scenario 23: Plugin Development Sprint

Scenario 24: Design Token Migration

💡 Tips for Effective Prompts

✅ Good Prompts

❌ Avoid Vague Prompts

🎯 Pro Tips

🔄 Integration with Other Tools

With Figma Official Dev Mode MCP

📚 More Examples