Figma MCP: The CTO's Guide to Design-to-Code in 2026

The design-to-code handoff has been the most expensive bottleneck in software development for as long as I can remember. Not because it's technically hard - because it's a translation problem. A designer creates a pixel-perfect mockup in Figma. A developer interprets it. They go back and forth for weeks. Spacing is off by 4px. The wrong shade of blue made it into production. The button has 12px padding instead of 16px. Nobody's happy.

I've watched this cycle burn thousands of engineering hours across the eight companies I've led over 16 years. The handoff problem isn't about skill - it's about information loss at every translation layer between design intent and production code.

Figma's MCP Server changes this equation. Not in the "this will revolutionize everything" marketing way - in the practical, measurable, "we actually shipped faster last sprint" way.

Two solid articles recently covered this topic. Felix Lee published a designer-focused playbook on ADPList's Substack walking through Claude Code integration step by step. Chinwike Maduabuchi wrote a more technical breakdown on LogRocket covering Figma file structure and MCP server setup with Cursor. Both are worth reading. Both are also incomplete.

Lee's article is optimized for designers who are new to coding - the "vibe-coding" crowd. It's a good 101 guide, but it doesn't address what happens when your Figma file has 200 components, your codebase has its own design system, and you need the AI to use your existing Button component instead of generating a new one from scratch. Maduabuchi's LogRocket piece goes deeper into file structure and shows a working React build from a sign-up page, but it stops short of the production realities - security implications, cost management, the bidirectional workflow Figma just launched, and the hard-earned lessons from teams that have actually shipped with this stack.

That's the gap I'm filling here. This guide covers what neither article does: the architectural decisions, the security considerations, the real cost math, the reverse workflow nobody's talking about, and the production lessons from teams like monday.com that discovered naive code generation doesn't work - and built something better.

What Figma MCP Actually Is - And What It Isn't

Let me be precise about this, because the marketing language around MCP gets fuzzy fast.

Model Context Protocol (MCP) is an open protocol - originally developed by Anthropic - that standardizes how AI agents access external tools and data sources. It's been called "the USB-C for AI apps," which is a decent analogy. Just as USB-C provides a universal connector between devices, MCP provides a universal interface between AI coding agents and external systems.

I wrote about MCP's broader architectural implications in my article on The Next Leap in LLM Architecture - Model Context Protocol. The core insight: MCP solves two fundamental problems that plague AI systems - context fragmentation (where models forget earlier context due to token limits) and task disorganization (where multiple agents work in silos, duplicating or misinterpreting objectives).

Figma's MCP Server - currently in beta - applies this protocol specifically to design data. It exposes your Figma design files as structured, machine-readable context that AI coding agents like Cursor, Claude Code, GitHub Copilot, VS Code, and others can consume directly.

Here's what matters: Figma MCP doesn't generate code itself. It provides structured design context to an AI agent, and the agent generates the code. The quality of that output depends on three things - the structure of your Figma file, the AI model you're using, and the prompts you write.

What the AI Agent Can Read From Your Figma File

Data Type	What's Extracted	Why It Matters
Layer names & hierarchy	Full node tree with semantic names	Maps directly to component structure and DOM hierarchy
Colors	Fills, strokes, opacity values	Generates accurate CSS color values and design tokens
Typography	Font family, size, weight, line height, letter spacing	Creates consistent text styles across components
Auto Layout settings	Direction, spacing, padding, alignment, constraints	Translates directly to CSS Flexbox/Grid properties
Component variants	Variant names, properties, boolean states	Generates component props, conditional rendering logic
Design tokens/variables	Semantic color names, spacing scales, radius values	Produces design system-aligned CSS variables or Tailwind config
Text content	All text strings in the design	Populates actual content in generated components
Effects	Shadows, blur, background blur	Generates box-shadow, filter, and backdrop-filter CSS

What It Cannot Read

Limitation	Impact	Workaround
Actual image pixels	Cannot extract raster images	Export assets manually to /public folder, reference in prompts
Prototype interactions	No hover states, transitions, or navigation flows	Describe interactions explicitly in your prompts or annotations
Comments & feedback	Design discussion context is invisible	Include relevant decisions in Figma annotations instead
Version history	No access to previous iterations	Reference specific frames if comparing versions
Complex animations	Motion design doesn't translate	Implement animations manually or use separate animation tools

This distinction matters more than most guides acknowledge. The ADPList article lists what Claude can and can't read in a simple diagram, which is helpful. But it doesn't explain the practical consequences - like the fact that you'll spend more time on image asset management than you expect, or that interaction design still requires explicit human communication.

The Three MCP Server Options (Not Just Two)

Most articles compare two options: Figma's official MCP server and Framelink. There's actually a third option worth knowing about, plus a critical architectural distinction between how they work.

Option 1: Figma's Official MCP Server

Figma's official server connects via two methods: a remote server (hosted by Figma, no desktop app required) or a desktop server (runs locally through the Figma desktop app). It provides a rich set of tools - 14 in total as of February 2026:

Tool	What It Does	When to Use It
generate_figma_design	Sends code UI back to Figma as editable layers (Claude Code only, remote only)	Reverse workflow - code to design
get_design_context	Extracts design context for a layer or selection	Primary tool for design-to-code
get_variable_defs	Returns variables and styles used in selection	Extracting design tokens
get_code_connect_map	Maps Figma nodes to codebase components	Ensuring component reuse
add_code_connect_map	Adds new Figma-to-code mappings	Building Code Connect library
get_screenshot	Takes screenshot of selection for layout fidelity	Visual reference for the AI agent
create_design_system_rules	Generates rule files for design system context	Setting up project-wide conventions
get_metadata	Returns sparse XML of layer IDs, names, types, positions	Breaking down large designs
get_figjam	Converts FigJam diagrams to XML	Architecture and flow diagrams
generate_diagram	Creates FigJam diagrams from Mermaid syntax	Generating visual documentation
whoami	Returns authenticated user identity	Debugging authentication
get_code_connect_suggestions	Suggests Figma-to-code component mappings	Automating Code Connect setup
send_code_connect_mappings	Confirms suggested Code Connect mappings	Finalizing component connections

The standout feature is Code Connect - the ability to map Figma components directly to your actual codebase components. According to Figma's own documentation:

"This is the #1 way to get consistent component reuse in code. Without it, the model is guessing."

That quote isn't marketing. It's the single most important sentence in Figma's entire MCP documentation. Without Code Connect, the AI agent doesn't know your Button component exists in src/components/ui/Button.tsx. It generates a new button from scratch every time. With Code Connect, it imports and uses your actual component with the correct props.

The catch: the official server's get_design_context tool outputs prescriptive React and Tailwind code by default. It generates things like leading-[22.126px] and text-[color:var(--neutral/dark-100%,black)] - arbitrary values that the AI agent copies verbatim instead of using your codebase's existing patterns. You can customize the framework output through your prompt (Vue, plain HTML+CSS, iOS), but the prescriptive nature remains.

Access: The remote server is available across all Figma plans. The desktop server requires a Dev or Full seat on a paid plan. Code Connect requires Organization or Enterprise plans.

Option 2: Framelink MCP for Figma (Community, Open-Source)

Framelink takes a fundamentally different approach. It's open-source (13,000+ GitHub stars), free, and works with any Figma account. It provides two core tools: get_figma_data for pulling structure, styling, and layout as JSON, and download_figma_images for fetching assets.

The critical difference is descriptive vs. prescriptive output. Framelink sends descriptive data - "this element has a 1px border and 16px padding" - and lets the AI agent decide how to implement it using your existing components, patterns, and conventions. The output is roughly 25% smaller than Figma's official MCP output, according to Framelink's own comparison.

From Framelink's documentation:

"Prescriptive output poisons the context. The LLM sees auto-generated code structure and mimics it instead of using your codebase's patterns. You end up refactoring the AI's output instead of just using it."

That's a strong claim, and in my testing, it holds up. When the AI agent receives prescriptive React code from Figma's official server, it tends to replicate that structure rather than adapting to your project's conventions. When it receives descriptive JSON from Framelink, it has more freedom to generate code that fits your architecture.

No Code Connect support, though. That's the trade-off.

Option 3: Cursor Talk To Figma MCP

There's a third option that neither the ADPList nor LogRocket articles mention: Cursor Talk To Figma MCP. It's more demanding to set up (requires a local server and a Figma plugin), but it works even without Dev Mode access. It's particularly useful for teams on Figma's free or Starter plans who can't access the official MCP server's full feature set.

The Comparison That Actually Helps You Decide

Aspect	Figma Official MCP	Framelink MCP	Cursor Talk To Figma
Output approach	Prescriptive (React/Tailwind code)	Descriptive (structured JSON)	Descriptive (via plugin API)
Code Connect	Yes - maps Figma to your real components	No	No
Output size	Larger, more verbose	~25% smaller	Varies
Style name preservation	Lost in output	Preserved	Preserved
Nested components	Flattened (can be misleading)	Accurately represented	Accurately represented
Cost	Free for remote; some features need paid plans	Free, open-source	Free, open-source
Setup complexity	OAuth flow, straightforward	API token, npm package	Local server + Figma plugin
Dev Mode required	Desktop server: yes; Remote: no (but limited)	No	No
Figma API rate limits	Same (depends on plan)	Same (depends on plan)	Bypasses API (uses plugin)
Tools available	14 tools + prompt resources	2 core tools + token generation	Direct Figma plugin access
Best for	Teams with Code Connect, paid Figma plans	Teams wanting AI to adapt to their codebase	Teams on free Figma plans

My recommendation: If you have a mature design system with Code Connect already configured, use Figma's official server - the component mapping alone justifies it. If you're working without Code Connect, or you want the AI to adapt to your existing codebase patterns rather than generating arbitrary Tailwind values, Framelink produces cleaner results. If you're on Figma's free plan and hitting API rate limits, Cursor Talk To Figma bypasses those limits entirely.

Critical note: Don't run multiple MCP servers simultaneously. Running two servers that access the same Figma data confuses your AI agent and produces inconsistent output. The LogRocket article mentions this, and I can confirm it from experience.

The Reverse Workflow Nobody's Covering: Code to Figma

Here's something neither the ADPList nor LogRocket articles address at all: the workflow now goes both directions.

On February 17, 2026, Figma announced the generate_figma_design tool - a Claude Code-exclusive feature that captures live, running UI from your browser and converts it into fully editable Figma layers. Dylan Field, Figma's CEO, framed it as escaping "tunnel vision":

"The design canvas is better at navigating lots of possibilities than prompting in an IDE. With the canvas you can think divergently and see the big picture by comparing approaches side by side."

The workflow: you build something in Claude Code, preview it in the browser, type "Send this to Figma," and the rendered UI becomes editable Figma frames. Text is editable text. Buttons are separate components. Layout uses Auto Layout. Your team can then annotate, explore variants, and iterate in Figma's canvas before sending changes back to code via the standard Figma MCP.

This creates a bidirectional loop:

Code (Claude Code) → Browser Preview → Figma (editable layers) ↓ Team annotates, explores ↓ Figma MCP → Code (updated implementation)

It's a compelling concept. But Builder.io's analysis raises legitimate concerns about the roundtrip friction:

"Count the tool switches in the full roundtrip: that's three distinct tools (Claude Code, browser, Figma) and at least five context switches. Each handoff loses information. Figma layers don't carry your business logic, event handlers, or state management."

They're right. Once you add business logic to generated code, design updates from Figma mean redoing those changes. The return trip still faces the same translation gap that MCP was supposed to solve.

My take: the code-to-Figma direction is valuable for exploration and stakeholder communication, not for production iteration. Use it when you need to show a PM or designer what you've built and get feedback in their preferred tool. Don't use it as a production workflow where code bounces back and forth between Figma and your IDE - the information loss at each handoff compounds.

Why Naive Code Generation Doesn't Work (And What monday.com Did Instead)

This is the section that separates production experience from tutorial content. And it's the lesson neither competitor article covers.

The monday.com engineering team tried the obvious approach first: paste a Figma link into Cursor, let it run using the Figma MCP, and generate code directly. The result?

"The generated output didn't use the design system components. Colors were hard-coded. Typography overrode the system defaults. CSS was written manually in places where it shouldn't have existed at all. From a distance, the result looked acceptable; from a system perspective, it was a mess."

The problem wasn't the AI model. The problem was that the model had no understanding of what their design system actually was. It didn't know which components existed, which props were valid, which tokens had to be used, or which accessibility rules were mandatory. Without that context, it guessed.

Their solution: they built a custom design-system MCP that represented their entire design system as structured, machine-readable knowledge. Then they built an agentic workflow using LangGraph - an 11-node graph where each node handles a single, well-defined part of the design-to-code translation:

Step	What It Does	Why It Matters
Figma data pull	Fetches raw design data via Figma MCP	Starting point for all analysis
Translation detection	Scans text nodes, identifies localization keys	Prevents hardcoded strings
Layout analysis	Studies spacing and positioning	Infers flex/grid structures correctly
Token resolution	Maps raw design values to semantic tokens	Uses design system tokens, not arbitrary values
Component identification	Determines which elements are design system components	Prevents duplicate component generation
Variant/prop resolution	Resolves valid variants and props for system components	Ensures correct component usage
Custom component planning	Plans CSS using tokens for non-system elements	Maintains design system consistency
Event fetching	Pulls analytics definitions from monday.com boards	Wires tracking events correctly
Usage example retrieval	Gets real usage examples from design system MCP	Provides implementation patterns
Accessibility resolution	Retrieves ARIA relationships, keyboard behavior	Bakes in a11y from the start
Implementation planning	Assembles complete build plan	Produces structured context, not code

The critical design decision: the agent returns structured context, not code. The code-generation model then formats the output according to the repository it's running in. This means the same design can produce different code for different microfrontends - each matching its own React version, design system version, and coding conventions.

The result:

"Developers spend far less time translating designs into implementation details. There's less back-and-forth with design, fewer review comments about incorrect components or props, and fewer late accessibility fixes after the feature is 'done.'"

This is the trajectory. The future of design-to-code isn't "AI generates code from a Figma link." It's "AI understands your design system, your codebase conventions, your accessibility requirements, and your component library - then generates code that fits all of them." We're not there yet with off-the-shelf MCP servers, but the direction is clear.

Structuring Your Figma File: The Non-Negotiables

Both competitor articles cover Figma file structure. The ADPList article provides a checklist. The LogRocket article demonstrates variables, Auto Layout, and components with a sign-up page example. Both are correct. But neither explains why each practice matters from a code generation perspective, or what specifically goes wrong when you skip them.

Here's the complete breakdown, informed by Figma's own documentation and real-world testing:

1. Semantic Layer Names → Component Names and CSS Classes

What to do: Name every layer with intent. hero-section, nav-links, cta-button, product-card, price-display.

What NOT to do: Leave default names. Frame 427, Group 5, Component 1, Rectangle 12.

Why it matters for code generation: Layer names become component names, CSS class names, and variable names in generated code. A layer named hero-section produces <section className="hero-section">. A layer named Frame 427 produces <div className="frame-427"> - meaningless in your codebase and impossible to maintain.

The fix for messy files: Figma AI can batch-rename layers. The Rename It community plugin also works well for bulk operations. Invest 30 minutes cleaning up names before running MCP - it saves hours of refactoring generated code.

2. Auto Layout on Everything → CSS Flexbox/Grid

What to do: Apply Auto Layout to every container, every section, every card, every form group. Set direction, spacing, padding, and alignment explicitly.

What NOT to do: Use absolute positioning, manual spacing, or overlapping layers.

Why it matters for code generation: Auto Layout maps directly to CSS Flexbox properties:

Auto Layout Property	CSS Equivalent	Example
Direction: Vertical	flex-direction: column	Stack items vertically
Direction: Horizontal	flex-direction: row	Items side by side
Gap	gap	Consistent spacing between items
Padding	padding	Container internal spacing
Alignment	align-items, justify-content	Item positioning within container
Fill container	flex: 1 or width: 100%	Responsive sizing
Hug contents	width: fit-content	Content-driven sizing

Without Auto Layout, the AI agent falls back to absolute positioning - position: absolute; top: 247px; left: 132px; - which is useless for responsive layouts and creates maintenance nightmares.

Figma's documentation recommends resizing the frame in Figma to check that it behaves as expected before generating code. This is a simple but powerful test: if your design breaks when you resize the frame, the generated code will break on different screen sizes too.

3. Components with Named Variants → React/Vue Props

What to do: Create components for every repeated element. Define variants with descriptive property names: size=sm|md|lg, state=default|hover|active|disabled, type=primary|secondary|ghost.

What NOT to do: Use variant=1, variant=2, variant=3. Or worse, duplicate elements without componentizing them.

Why it matters for code generation: Variant names become component props in generated code. size=lg produces <Button size="lg">. variant=2 produces <Button variant="2"> - which tells the developer nothing about what that variant represents.

The LogRocket article demonstrates this with a button component that includes default, full-width, and loading states. The generated code correctly maps these to logical UI states. That's the ideal outcome - but it only works when variants are named semantically.

4. Design Tokens as Figma Variables → CSS Variables / Tailwind Config

What to do: Define all colors, spacing values, typography scales, and border radii as Figma variables. Use semantic naming: color-primary, spacing-md, radius-lg, font-heading-xl.

What NOT to do: Hardcode hex values, pixel values, or font sizes directly on elements.

Why it matters for code generation: Figma variables get extracted into your CSS custom properties or Tailwind configuration. A variable named color-primary with value #2563EB produces:

:root {
  --color-primary: #2563EB;
}

Or in Tailwind:

module.exports = {
  theme: {
    extend: {
      colors: {
        primary: '#2563EB',
      }
    }
  }
}

Hardcoded values produce hardcoded CSS - color: #2563EB; scattered across every component - which defeats the purpose of a design system entirely.

The LogRocket article recommends the Open Variable Visualizer plugin to export Figma variables as JSON plus a resolver utility file. This is a solid approach. Those two files give your AI agent a complete design system reference before it even looks at the layout.

5. Annotations for Behavior → AI Understanding of Intent

What to do: Add Figma annotations to describe behavior that can't be captured visually: hover states, transitions, responsive breakpoints, loading states, error states, keyboard navigation patterns.

What NOT to do: Assume the AI will infer interaction behavior from static designs.

Why it matters: The AI agent sees a static snapshot. It doesn't know that a button should have a hover state with opacity: 0.9 and a 150ms ease-in-out transition. It doesn't know that a form should show inline validation errors below each field. Annotations bridge this gap.

This is currently only supported by the official Figma MCP server. Framelink doesn't read annotations. If you're using Framelink, include behavioral descriptions in your prompts instead.

The Real Cost Math

Neither competitor article discusses costs in detail. Here's what you're actually paying for:

Figma Plan Costs

Plan	Monthly Cost (per editor)	MCP Access	Dev Mode	Code Connect	API Rate Limits
Free/Starter	$0	Remote only (limited)	No	No	Strict - expect 429 errors after ~2 hours
Professional	$15/editor	Remote + Desktop	With Dev seat	No	Standard
Organization	$45/editor	Full access	Included	Yes	Higher
Enterprise	$75/editor	Full access	Included	Yes	Highest

The ByteMinds analysis confirms a real-world pain point: "After a couple of hours of work on a free plan, you'll likely get a '429 – too many requests' system message. This is a Figma API limitation, not MCP." Full-fledged work requires a paid plan.

AI Agent Token Costs

Every MCP interaction consumes tokens in your AI coding agent. Real-world examples from the ByteMinds article:

Task	Tokens Used	Approximate Cost
Single card component	161,900 tokens	~$0.10
Full homepage (6 sections)	433,600 tokens	~$0.21
Complex multi-page app	1M+ tokens	~$0.50-$2.00

These costs are per generation attempt. If you need 3-4 iterations to get the output right, multiply accordingly. For a team generating 20-30 components per week, monthly AI token costs can reach $50-200 depending on complexity and iteration count.

The Hidden Cost: Refactoring Time

The biggest cost isn't Figma subscriptions or AI tokens - it's the time developers spend refactoring generated code. Without Code Connect, the AI generates new components from scratch instead of using your existing ones. Without proper design tokens, it hardcodes values. Without semantic layer names, it produces meaningless class names.

The monday.com team discovered this the hard way: "The developer still had to refactor the generated code into real components, remove invalid styles, and hope nothing broke along the way." Their solution - building a custom design-system MCP - was expensive upfront but eliminated the refactoring loop entirely.

For most teams, the pragmatic approach is:

1. Start with proper Figma file structure (free, high impact)
2. Set up Code Connect if on Organization/Enterprise plan (medium cost, very high impact)
3. Create project-specific rules files for your AI agent (free, high impact)
4. Consider a custom design-system MCP only if you're generating code at scale (high cost, highest impact)

Security: The Section Nobody Wants to Write

I've written extensively about server hardening and infrastructure security. The security implications of MCP servers deserve the same rigor - and neither competitor article addresses them at all.

Authentication Token Risks

The official Figma MCP uses OAuth for authentication. Framelink requires a personal access token with broad permissions. The ADPList article instructs readers to generate a personal access token and paste it into a CLI command:

claude mcp add figma -e FIGMA_PERSONAL_ACCESS_TOKEN=figd_xxxxxxxxxx

That token, if compromised, grants access to every Figma file in your workspace. In a team environment with sensitive design files - unreleased product designs, client work under NDA, internal tools - this is a significant risk.

Best practices:

• Scope tokens to minimum required permissions
• Set reasonable expiration dates (not "never expires")
• Store tokens in environment variables or secrets managers, not in shell history or config files checked into git
• Rotate tokens regularly
• Use the official OAuth flow when possible (it's more secure than personal access tokens)

Supply Chain Vulnerabilities

MCP servers are npm packages. They carry the same supply chain risks as any dependency in your project. A 2026 audit of 68 MCP packages found 118 security findings, including dependency chain risks where the package itself was fine but its transitive dependencies introduced vulnerabilities.

Specifically for Figma MCP:

• CVE-2025-15061 (Framelink): A remote code execution vulnerability in the fetchWithRetry method allowed attackers to execute arbitrary code on affected installations without authentication. It's been patched, but it demonstrates the risk.
• CVE-2025-53967 (Figma official): A vulnerability was discovered in Figma's own MCP server, reinforcing that even first-party servers aren't immune.
• A security assessment of figma-console-mcp identified three critical vulnerabilities that, when combined, could enable interception, exfiltration, and poisoning of design data flowing between AI assistants and Figma.

The broader MCP security landscape is concerning. Adversa AI's February 2026 report notes: "The attack surface is expanding to include complex server-side vulnerabilities and supply chain risks." Dark Reading reported that researchers found serious vulnerabilities in popular MCP servers from both Microsoft and Anthropic.

What this means for your team:

Risk	Likelihood	Impact	Mitigation
Token theft	Medium	High - full workspace access	Scope permissions, rotate regularly, use OAuth
Supply chain attack	Low-Medium	Critical - arbitrary code execution	Pin versions, audit dependencies, monitor advisories
Data exfiltration	Low	High - design IP leakage	Review MCP server source code, use official servers when possible
Prompt injection	Medium	Medium - incorrect code generation	Validate generated code, don't auto-commit
API rate limit abuse	Medium	Low - service disruption	Monitor usage, implement rate limiting at project level

Treat MCP servers like any other integration surface. Pin versions. Review changelogs before updating. Monitor security advisories. And never grant broader permissions than necessary. The same principles I outlined in my cybersecurity infrastructure guide apply here - defense in depth, principle of least privilege, and assume breach.

Setting Up the Complete Workflow: Step by Step

Let me walk through the actual setup. Both competitor articles cover this, but I'll add the details they skip - particularly around rules files, responsive design handling, and the prompting patterns that produce the best results.

Step 1: Choose Your MCP Server and Connect

For Figma's Official MCP (Remote):

If you're using Cursor, use the deep link to add it directly. If you're using Claude Code:

# Add for current project
claude mcp add --transport http figma https://mcp.figma.com/mcp

# Or add globally
claude mcp add --scope user --transport http figma https://mcp.figma.com/mcp

Then authenticate via /mcp → select figma → Authenticate → complete OAuth in browser.

For Framelink MCP:

Add to your IDE's mcp.json:

{
  "mcpServers": {
    "Framelink MCP for Figma": {
      "command": "npx",
      "args": [
        "-y",
        "figma-developer-mcp",
        "--figma-api-key=YOUR-KEY",
        "--stdio"
      ]
    }
  }
}

Replace YOUR-KEY with your Figma personal access token. On Windows, use "command": "cmd" and add "/c", "npx" to the args array.

Verify connection: Run /mcp in your IDE. You should see a green status indicator.

Step 2: Create Your Rules File

This is the step both competitor articles underplay. A rules file tells the AI agent your project conventions, coding standards, and design system requirements. Without it, the agent generates generic code that doesn't match your codebase.

Create a .cursor/rules/ directory (for Cursor) or equivalent for your IDE. Here's a production-tested rules template:

# Project Rules for Figma MCP Code Generation

## Stack
- Framework: Next.js 14 (App Router)
- Styling: Tailwind CSS v3
- Language: TypeScript (strict mode)
- Component library: Custom design system in src/components/ui/

## Code Standards
- Use named exports only; no default exports
- Use `type` over `interface` (except for class implementations)
- Prefer guard clauses over nested conditionals
- All components must be typed with TypeScript

## Styling Rules
- Mobile-first responsive approach
- Use Tailwind utility classes; avoid arbitrary values like p-[40px]
- Extract repeated colors to CSS variables in src/styles/globals.css
- Use design system spacing scale: 4, 8, 12, 16, 24, 32, 48, 64
- Never hardcode hex colors; always reference design tokens

## Accessibility
- Use semantic HTML (section, article, nav, header, footer, main)
- Every image must have descriptive alt text
- Interactive elements must be keyboard accessible
- Follow proper heading hierarchy (don't skip levels)
- Include ARIA labels for icon-only buttons

## Component Patterns
- Check src/components/ui/ before creating new components
- Reuse existing components; extend with variants if needed
- Props should use union types, not enums
- Include loading, error, and empty states

## Images
- Do not download images from Figma
- Use placeholder images from src/assets/
- Always specify width and height attributes

## Workflow
- Skip linter checks during generation
- Skip dependency verification unless errors occur
- Focus on core implementation first, refinements second

This single file dramatically improves output quality. The ByteMinds article confirms: "It's important to understand that the agent may selectively ignore formal rules. Sometimes, it makes sense to duplicate important requirements directly in the prompt."

Step 3: Extract Design Tokens First

Before generating any components, extract your design system:

Read my Figma design at: [PASTE FIGMA URL] Extract all design tokens and create: 1. A Tailwind config extending the default theme with: - Custom colors matching the design (use semantic names) - Font families and sizes - Spacing scale - Border radius values 2. CSS variables file as fallback Reference the rules in .cursor/rules/ for naming conventions.

This gives the AI agent a foundation. Every subsequent component generation will reference these tokens instead of hardcoding values.

Step 4: Build Components Incrementally

For a single component:

Look at the [COMPONENT NAME] in my Figma file: [PASTE FRAME URL] Generate a React component that: - Matches the exact styling from the design - Uses the Tailwind config we created - Supports all variants defined in Figma - Is fully typed with TypeScript - Includes hover/focus states - Follows the patterns in .cursor/rules/ - Check src/components/ui/ for existing components to reuse

For responsive layouts (provide both mobile and desktop frames):

Implement the component design from Figma: - Mobile: [MOBILE FRAME URL] - Desktop: [DESKTOP FRAME URL] Ensure responsive design: mobile-first, switch to desktop layout at 1024px. Use Tailwind responsive prefixes (md:, lg:).

This responsive pattern - providing separate mobile and desktop frame URLs - is something the ByteMinds article highlights as working particularly well. The AI handles responsive breakpoints much better when it can see both layouts explicitly.

Step 5: Review, Don't Trust

Every piece of generated code needs human review. Check for:

• Correct component reuse (not duplicating existing components)
• Design token usage (not hardcoded values)
• Semantic HTML structure
• Accessibility attributes
• Responsive behavior at all breakpoints
• TypeScript types (no any)
• Import paths matching your project structure

The 85% Rule: What Figma MCP Gets Right and What It Doesn't

After extensive testing, here's my honest assessment of where Figma MCP excels and where it falls short:

What It Gets Right (~85% of the time)

Capability	Accuracy	Notes
Color extraction	95%+	Excellent when using Figma variables
Typography	90%+	Font family, size, weight, line height
Spacing and padding	90%+	When Auto Layout is used consistently
Component structure	85%+	Basic component hierarchy
Flexbox layout	85%+	Direction, gap, alignment from Auto Layout
Text content	95%+	All text strings extracted accurately
Design token mapping	80%+	When variables are properly defined

What It Gets Wrong (~15% requiring manual work)

Issue	Frequency	Impact	Fix
Responsive breakpoints	Common	Medium	Provide both mobile and desktop frames
Interaction states	Always	High	Describe in prompts or annotations
Image handling	Always	Medium	Export manually, reference in prompts
Complex animations	Always	High	Implement manually
Border radius placement	Occasional	Low	Quick CSS fix
Component reuse	Common (without Code Connect)	High	Set up Code Connect or use rules file
Accessibility	Common	High	Add ARIA attributes manually or via rules
State management	Always	High	Business logic is always manual

The LogRocket article reports "near pixel-perfect" results with a caveat: corner radii ended up on the wrong side of an image, and mobile responsiveness required additional prompting. That matches my experience exactly. The 85% that works is genuinely impressive. The 15% that doesn't is predictable and manageable - as long as you know it's coming.

AI Model Selection: It Matters More Than Your MCP Choice

The LogRocket article recommends switching from Cursor's "Auto" mode to a Claude model. I'll go further with specific recommendations based on testing:

Model	Strengths for Design-to-Code	Weaknesses	Best For
Claude 4 Sonnet	Clean component structure, excellent TypeScript, strong Tailwind	Can be verbose	Complex components with many variants
Claude 4.5 Sonnet	Most accurate overall, best reasoning	Higher token cost	Production-critical code generation
GPT-4o	Good general output, fast	Less consistent with design tokens	Quick prototyping
Claude 3.5 Sonnet	Reliable, cost-effective	Older model, less nuanced	Budget-conscious teams

For React and Tailwind generation specifically, Claude models consistently produce cleaner, more structured code. They handle component composition, TypeScript typing, and responsive patterns better than alternatives. This aligns with what I've observed in prompt engineering more broadly - model selection is often the highest-leverage decision you can make.

When NOT to Use Figma MCP

Figma MCP isn't the right tool for every situation. Here's the honest breakdown:

Don't use it when:

• Your Figma file is messy. Unstructured layers, no Auto Layout, no variables, no components. Fix the file first. As the ByteMinds article puts it: "With a messy design, you might get nowhere. Or rather, you might get something, but fixing the errors will take longer than if you did everything manually."
• The component is highly interactive. Complex animations, drag-and-drop interfaces, real-time data visualizations. MCP can't read prototype interactions, so you'll spend more time describing behavior in prompts than just building it manually.
• The task is small and targeted. Changing a button's padding from 12px to 16px. Adding a border to a card. For micro-tasks, opening Figma and your IDE is more overhead than just writing the CSS.
• Your codebase has highly custom architecture. If your project uses a custom rendering engine, non-standard component patterns, or framework-specific abstractions that AI agents don't understand, the generated code will need so much refactoring that the time savings evaporate.
• Security sensitivity is paramount. If your design files contain unreleased product designs, client work under NDA, or sensitive business information, evaluate the token security and data flow implications before connecting them to third-party MCP servers.

Use it when:

• You have a mature Figma design system with variables, components, Auto Layout, and semantic naming.
• You're building new pages or sections from established design patterns.
• You're a solo developer or small team where the same person designs and codes.
• You're prototyping rapidly and speed matters more than perfect code architecture.
• You're building a component library from Figma design system specs.
• You need to onboard new developers by generating reference implementations from designs.

The Bigger Picture: Where This Is Heading

The traditional designer-to-developer handoff is evolving into a designer-to-agent handoff. That's not marketing speak - it's a real shift in how design intent gets translated into production code. I've been tracking this evolution since I wrote about Model Context Protocol's architectural implications, and the trajectory is clear.

The monday.com case study shows where this is heading: AI agents that don't just read Figma files but understand your entire design system, your codebase conventions, your accessibility requirements, and your component library. Their 11-node agentic workflow produces code that "looks like it was written by someone who deeply understands the system." That's the standard - and it's achievable today with enough investment in tooling.

For most teams, the pragmatic path is:

1. Now: Structure your Figma files properly. Set up an MCP server. Create rules files. Start generating components incrementally.
2. Next quarter: Implement Code Connect if on a compatible Figma plan. Build project-specific prompt templates. Measure time savings.
3. This year: Evaluate whether a custom design-system MCP (like monday.com's approach) makes sense for your scale.

The teams that learn to design for AI consumption - with structured files, semantic naming, proper tokens, and explicit behavioral annotations - will ship faster. The teams that don't will keep burning weeks in handoff loops.

But let's keep perspective. Figma MCP is a tool, not a replacement for engineering judgment. It accelerates the translation layer between design and code. It doesn't make architectural decisions, handle state management, optimize performance, or write tests. Those remain human responsibilities - at least for now.

The design-to-code gap has been one of the most persistent friction points in software development. Figma MCP doesn't eliminate it, but it narrows it significantly. And in an industry where shipping speed compounds, that narrowing matters.