What can figma-mcp-server do?

figma document tree traversal and introspection, component variant enumeration and property extraction, design token extraction and standardization, frame and artboard content export with metadata, mcp tool registration and schema generation, local caching and offline document access, cursor and claude desktop integration, gemini cli integration and command-line tool invocation

figma-mcp-server

MCP ServerFree

A comprehensive local MCP server for Figma. Connect Figma with the Gemini CLI, Cursor, and Claude Desktop.

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

figma document tree traversal and introspection

Medium confidence

Exposes Figma's document hierarchy as queryable data structures through MCP tools, allowing clients to recursively traverse frames, components, groups, and design tokens without manual API pagination. Implements a local caching layer that mirrors the Figma REST API response structure, enabling fast repeated access to design system metadata without rate-limit pressure on Figma's servers.

Solves for

I need to programmatically inspect a Figma file's structure to understand component hierarchy and naming conventionsI want to extract all component metadata (names, properties, variants) from a design file to generate code or documentationI need to find specific frames or components by name or property to automate design-to-code workflows

Best for

Design system teams automenting component inventory and documentation

Developers building design-to-code generators that need to query Figma structure

AI agents (Claude, Gemini) that need to understand design context before generating UI code

Requires

Node.js 16+

Figma API token (personal access token or OAuth token)

Figma file ID (accessible from Figma URL)

Limitations

Requires valid Figma API token with read access to target files

Document tree is cached at server startup — real-time changes to Figma require server restart or manual refresh

Large files (1000+ frames) may cause initial traversal latency of 2-5 seconds

What makes it unique

Implements MCP as a bridge between Figma's REST API and LLM clients, caching the full document tree locally to avoid repeated API calls and enabling stateless tool invocations from Claude/Gemini without managing session state

vs alternatives

Unlike direct Figma API clients, this MCP server abstracts authentication and pagination, allowing AI agents to query design files with simple tool calls while respecting Figma's rate limits through local caching

component variant enumeration and property extraction

Medium confidence

Automatically discovers and catalogs all component variants within a Figma file, extracting variant properties (color, size, state) and their corresponding design tokens. Uses Figma's component set structure to build a queryable registry that maps variant combinations to visual properties, enabling code generators to understand design system constraints and generate type-safe component APIs.

Solves for

I need to generate TypeScript component props interfaces that match all available variants in our design systemI want to create a component inventory that lists every variant combination and its visual propertiesI need to validate that my code components match the variants defined in Figma

Best for

Design system maintainers building component libraries with strict variant coverage

Code generators (Storybook, Chromatic, Penpot) that need to enumerate all component states

Teams using design tokens and needing to sync variant properties to code

Requires

Figma file with properly structured components and variants

Read access to component definitions via Figma API token

Limitations

Only works with Figma components (not instances) — requires proper component setup in Figma

Variant property extraction depends on consistent naming conventions in Figma

Does not extract computed styles (shadows, effects) — only explicit variant properties

What makes it unique

Parses Figma's component variant naming syntax to automatically extract property dimensions and values, then maps these to design tokens, enabling bidirectional sync between design and code without manual configuration

vs alternatives

More comprehensive than Figma's native variant export because it builds a queryable registry with token mappings, allowing AI agents to reason about variant coverage and generate exhaustive component tests

design token extraction and standardization

Medium confidence

Extracts design tokens (colors, typography, spacing, shadows) from Figma's native token system or from component properties, normalizing them into a standardized JSON format compatible with design token standards (W3C Design Tokens, Tokens Studio). Implements token aliasing and hierarchical organization to map Figma's visual properties to semantic token names usable in code.

Solves for

I need to export our Figma design tokens to CSS variables, Tailwind config, or design token JSONI want to sync design token changes from Figma into our codebase automaticallyI need to validate that our code is using only approved design tokens from Figma

Best for

Design systems teams managing tokens across Figma and multiple code repositories

Frontend teams building token-driven component libraries

Organizations standardizing on W3C Design Tokens format

Requires

Figma file with design tokens defined (native or via Tokens Studio plugin)

Figma API token with read access

Limitations

Requires Figma Tokens plugin or manual token setup in Figma — no automatic token detection from raw colors

Token hierarchy and naming conventions must be consistent in Figma to extract correctly

Does not support computed tokens (e.g., 'color-primary-hover' derived from 'color-primary')

What makes it unique

Implements token normalization that converts Figma's native token format into W3C-compliant JSON, preserving semantic relationships and enabling downstream tooling (Tokens Studio, Style Dictionary) to consume the output without custom parsing

vs alternatives

Unlike manual token export or Figma plugins that generate CSS, this MCP server produces portable JSON that works with any design token framework and integrates seamlessly with AI agents that need to reason about design constraints

frame and artboard content export with metadata

Medium confidence

Exports individual Figma frames or artboards as structured data including layout information, child elements, text content, and visual properties. Implements a recursive export strategy that preserves the design hierarchy while flattening it into queryable JSON, enabling code generators to understand page structure and generate corresponding HTML/React layouts.

Solves for

I need to convert a Figma frame into HTML or React JSX with proper structure and stylingI want to extract all text content from a frame to generate copy or translationsI need to understand the layout structure (grid, flex, absolute positioning) of a design to generate responsive code

Best for

Design-to-code teams automating UI generation from Figma designs

Content teams extracting copy and translations from design files

Developers building design preview systems that need to understand frame structure

Requires

Figma file with frames or artboards

Frame ID or frame name for targeting

Limitations

Export is metadata-only — does not generate actual HTML/CSS, only structure and properties

Complex layout constraints (auto-layout, constraints) are exported as properties but not interpreted

Images and assets are referenced by URL only — not embedded in export

What makes it unique

Preserves Figma's hierarchical structure in JSON while flattening it for code generation, including auto-layout metadata that enables downstream tools to infer responsive behavior without manual layout interpretation

vs alternatives

More structured than screenshot-based design-to-code because it exports semantic layout information, allowing AI agents to generate semantically correct HTML rather than pixel-based approximations

mcp tool registration and schema generation

Medium confidence

Implements the Model Context Protocol server interface, automatically registering Figma operations as callable tools with JSON Schema definitions. Handles request/response serialization, error handling, and tool discovery, allowing Claude, Gemini, and other MCP-compatible clients to invoke Figma operations as first-class functions without custom integration code.

Solves for

I want Claude or Gemini to be able to query my Figma file directly without me writing custom API codeI need to expose Figma operations as tools in my AI agent or agentic workflowI want to use Cursor or Claude Desktop to interact with Figma files through natural language

Best for

AI agent developers building multi-tool workflows that include design system access

Teams using Claude Desktop or Cursor that want Figma integration

Developers building LLM-powered design automation tools

Requires

Node.js 16+

MCP-compatible client (Claude Desktop, Cursor, Gemini CLI, or custom MCP client)

Figma API token

Limitations

MCP protocol overhead adds ~50-100ms per tool invocation compared to direct API calls

Tool discovery and schema validation happens at server startup — adding new tools requires server restart

No built-in authentication beyond Figma API token — requires secure token management in deployment

What makes it unique

Implements the full MCP server lifecycle (initialization, tool registration, request handling, error propagation), abstracting the protocol complexity so Figma operations appear as native tools to LLM clients without custom middleware

vs alternatives

Unlike REST API wrappers or custom integrations, MCP server registration enables seamless tool discovery and invocation in Claude Desktop and Cursor, reducing friction for non-technical users to access Figma programmatically

local caching and offline document access

Medium confidence

Maintains a local in-memory cache of Figma document structure and metadata, populated at server startup from the Figma API. Enables repeated queries without hitting Figma's rate limits and provides offline access to cached data after initial sync. Implements cache invalidation strategies (TTL, manual refresh) to balance freshness with performance.

Solves for

I want to query my Figma file multiple times without hitting rate limitsI need fast responses for design queries without waiting for Figma API latencyI want to work with Figma data even if my connection to Figma is temporarily unavailable

Best for

High-frequency design-to-code workflows that query the same file repeatedly

Teams with strict API rate limit budgets

Developers building interactive tools that need sub-100ms response times

Requires

Initial Figma API call to populate cache

Sufficient memory for document tree (typically <50MB for most files)

Limitations

Cache is in-memory only — lost on server restart, no persistence to disk

Real-time Figma changes are not reflected until manual cache refresh or server restart

Large files (1000+ frames) consume significant memory — no cache size limits or eviction

What makes it unique

Implements a simple in-memory cache that mirrors Figma's API response structure, allowing clients to query cached data without pagination or authentication overhead while maintaining API token security on the server

vs alternatives

More efficient than repeated API calls for high-frequency queries, but less sophisticated than distributed caching systems — suitable for single-server deployments where cache consistency is not critical

cursor and claude desktop integration

Medium confidence

Provides native integration with Cursor IDE and Claude Desktop through MCP protocol, enabling users to invoke Figma queries directly from the editor or chat interface. Implements context injection that allows Figma data to be referenced in code generation prompts, and supports tool invocation from natural language queries without explicit API calls.

Solves for

I want to ask Claude in my editor 'what components are in this Figma file' and get an answerI need to generate code based on a Figma design while working in CursorI want to reference design tokens from Figma in my code generation prompts

Best for

Cursor users building design-to-code workflows

Claude Desktop users managing design systems

Teams where designers and developers collaborate in the same tools

Requires

Cursor IDE 0.30+ or Claude Desktop 0.5+

figma-mcp-server running locally or accessible via network

Figma API token configured in server

Limitations

Requires Cursor 0.30+ or Claude Desktop 0.5+ — older versions do not support MCP

Tool invocation depends on LLM understanding of tool schemas — complex queries may not invoke correct tools

No built-in UI for browsing Figma files — all interaction is through text prompts

What makes it unique

Bridges the gap between design and code by making Figma a first-class data source in Cursor and Claude Desktop, allowing developers to reference design context in code generation without context switching to Figma

vs alternatives

Unlike manual design-to-code workflows or separate design tools, this integration embeds Figma queries directly in the IDE, reducing friction and enabling AI-assisted code generation that respects design constraints

gemini cli integration and command-line tool invocation

Medium confidence

Exposes Figma operations as command-line tools accessible through the Gemini CLI, enabling shell scripts and CI/CD pipelines to query Figma programmatically. Implements tool invocation through standard input/output, allowing Figma data to be piped into other CLI tools for automated design system workflows.

Solves for

I want to query Figma from a shell script or CI/CD pipelineI need to generate code or documentation from Figma as part of my build processI want to validate design system compliance by checking Figma against code

Best for

DevOps teams automating design system synchronization in CI/CD

Build systems that need to generate code from Figma designs

Teams using Gemini CLI for multi-tool automation

Requires

Gemini CLI installed and configured

figma-mcp-server running and accessible

Figma API token

Limitations

Requires Gemini CLI to be installed and configured

Tool output is JSON only — requires jq or similar tools for text processing

No streaming responses — large exports may timeout or exceed memory limits

What makes it unique

Exposes MCP tools through Gemini CLI's command-line interface, enabling shell-based automation and CI/CD integration without custom scripting or API client libraries

vs alternatives

More scriptable than GUI-based Figma access, and more flexible than Figma's native webhooks because it allows on-demand queries rather than event-driven updates

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with figma-mcp-server, ranked by overlap. Discovered automatically through the match graph.

MCP Server41

Figma-Context-MCP

MCP server to provide Figma layout information to AI coding agents like Cursor

component hierarchy and property extractiondesign data simplification and metadata filteringfigma design data extraction with css translation

3 shared capabilities

MCP Server27

figma-mcp

ModelContextProtocol for Figma's REST API

figma document tree traversal and introspectionvariant and variant group resolutiondesign token extraction and structured export

3 shared capabilities

MCP Server28

figma-mcp

ModelContextProtocol server for Figma

design token extraction and synchronizationfigma document tree traversal and introspection via mcpcomponent instance analysis and dependency mapping

3 shared capabilities

MCP Server21

@iflow-mcp/figma-mcp

ModelContextProtocol server for Figma

component and design token extractionfigma document introspection via mcp protocol

2 shared capabilities

MCP Server46

Figma MCP Server

Read Figma designs, components, and design tokens via MCP.

component metadata extraction and queryingfigma file structure introspection and traversal

2 shared capabilities

Extension41

CodeParrot AI: Figma to Code || Design To Code Copilot

Code Parrot converts Design to code. Get production ready UI components from Figma files or Images. Supports React, Flutter, HTML and more. Ship stunning UI lightning Fast.

component variant and state generation from figma component setsdesign system token extraction and code generation

2 shared capabilities

Best For

✓Design system teams automenting component inventory and documentation
✓Developers building design-to-code generators that need to query Figma structure
✓AI agents (Claude, Gemini) that need to understand design context before generating UI code
✓Design system maintainers building component libraries with strict variant coverage
✓Code generators (Storybook, Chromatic, Penpot) that need to enumerate all component states
✓Teams using design tokens and needing to sync variant properties to code
✓Design systems teams managing tokens across Figma and multiple code repositories
✓Frontend teams building token-driven component libraries

Known Limitations

⚠Requires valid Figma API token with read access to target files
⚠Document tree is cached at server startup — real-time changes to Figma require server restart or manual refresh
⚠Large files (1000+ frames) may cause initial traversal latency of 2-5 seconds
⚠No built-in filtering or search optimization — full tree traversal required for complex queries
⚠Only works with Figma components (not instances) — requires proper component setup in Figma
⚠Variant property extraction depends on consistent naming conventions in Figma

Requirements

Node.js 16+Figma API token (personal access token or OAuth token)Figma file ID (accessible from Figma URL)Figma file with properly structured components and variantsRead access to component definitions via Figma API tokenFigma file with design tokens defined (native or via Tokens Studio plugin)Figma API token with read accessFigma file with frames or artboards

Input / Output

Accepts: Figma file ID (string), Query parameters (frame name, component name, property filters), Component ID or component name (string), Variant property filters (optional), Figma file ID, Token group or category filter (optional), Frame ID (string), Export options (include children, include text, include styles), MCP tool call requests with JSON parameters, Figma file ID for cache population, Natural language queries in Cursor or Claude Desktop chat, Command-line arguments (file ID, query parameters)

Produces: JSON tree structure with node metadata, Structured component definitions with properties and variants, Design token definitions (colors, typography, spacing), JSON array of variant objects with property mappings, TypeScript interface definitions for component props, Design token references for each variant, JSON in W3C Design Tokens format, Flat or hierarchical token object, Token metadata (description, category, usage), JSON object with frame metadata and child hierarchy, Text content array with styling information, Layout properties (position, size, constraints), JSON responses conforming to MCP protocol, Error messages with MCP error codes, Cached document metadata (in-memory, not directly exposed), Text responses with Figma data, Code snippets generated based on design context, Design token references in code, JSON output to stdout, Error messages to stderr

UnfragileRank

Adoption30%(30% weight)

Quality25%(25% weight)

Ecosystem42%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

Visit figma-mcp-server→

Package Details

npm

Registry

2.1.1

Version

1,589

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About

A comprehensive local MCP server for Figma. Connect Figma with the Gemini CLI, Cursor, and Claude Desktop.

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Capabilities8 decomposed

figma document tree traversal and introspection

Medium confidence

Solves for

Best for

Design system teams automenting component inventory and documentation

Developers building design-to-code generators that need to query Figma structure

AI agents (Claude, Gemini) that need to understand design context before generating UI code

Requires

Node.js 16+

Figma API token (personal access token or OAuth token)

Figma file ID (accessible from Figma URL)

Limitations

Requires valid Figma API token with read access to target files

Document tree is cached at server startup — real-time changes to Figma require server restart or manual refresh

Large files (1000+ frames) may cause initial traversal latency of 2-5 seconds

What makes it unique

vs alternatives

component variant enumeration and property extraction

Medium confidence

Solves for

Best for

Design system maintainers building component libraries with strict variant coverage

Code generators (Storybook, Chromatic, Penpot) that need to enumerate all component states

Teams using design tokens and needing to sync variant properties to code

Requires

Figma file with properly structured components and variants

Read access to component definitions via Figma API token

Limitations

Only works with Figma components (not instances) — requires proper component setup in Figma

Variant property extraction depends on consistent naming conventions in Figma

Does not extract computed styles (shadows, effects) — only explicit variant properties

What makes it unique

vs alternatives

design token extraction and standardization

Medium confidence

Solves for

Best for

Design systems teams managing tokens across Figma and multiple code repositories

Frontend teams building token-driven component libraries

Organizations standardizing on W3C Design Tokens format

Requires

Figma file with design tokens defined (native or via Tokens Studio plugin)

Figma API token with read access

Limitations

Requires Figma Tokens plugin or manual token setup in Figma — no automatic token detection from raw colors

Token hierarchy and naming conventions must be consistent in Figma to extract correctly

Does not support computed tokens (e.g., 'color-primary-hover' derived from 'color-primary')

What makes it unique

vs alternatives

frame and artboard content export with metadata

Medium confidence

Solves for

Best for

Design-to-code teams automating UI generation from Figma designs

Content teams extracting copy and translations from design files

Developers building design preview systems that need to understand frame structure

Requires

Figma file with frames or artboards

Frame ID or frame name for targeting

Limitations

Export is metadata-only — does not generate actual HTML/CSS, only structure and properties

Complex layout constraints (auto-layout, constraints) are exported as properties but not interpreted

Images and assets are referenced by URL only — not embedded in export

What makes it unique

vs alternatives

More structured than screenshot-based design-to-code because it exports semantic layout information, allowing AI agents to generate semantically correct HTML rather than pixel-based approximations

mcp tool registration and schema generation

Medium confidence

Solves for

Best for

AI agent developers building multi-tool workflows that include design system access

Teams using Claude Desktop or Cursor that want Figma integration

Developers building LLM-powered design automation tools

Requires

Node.js 16+

MCP-compatible client (Claude Desktop, Cursor, Gemini CLI, or custom MCP client)

Figma API token

Limitations

MCP protocol overhead adds ~50-100ms per tool invocation compared to direct API calls

Tool discovery and schema validation happens at server startup — adding new tools requires server restart

No built-in authentication beyond Figma API token — requires secure token management in deployment

What makes it unique

vs alternatives

local caching and offline document access

Medium confidence

Solves for

Best for

High-frequency design-to-code workflows that query the same file repeatedly

Teams with strict API rate limit budgets

Developers building interactive tools that need sub-100ms response times

Requires

Initial Figma API call to populate cache

Sufficient memory for document tree (typically <50MB for most files)

Limitations

Cache is in-memory only — lost on server restart, no persistence to disk

Real-time Figma changes are not reflected until manual cache refresh or server restart

Large files (1000+ frames) consume significant memory — no cache size limits or eviction

What makes it unique

vs alternatives

cursor and claude desktop integration

Medium confidence

Solves for

Best for

Cursor users building design-to-code workflows

Claude Desktop users managing design systems

Teams where designers and developers collaborate in the same tools

Requires

Cursor IDE 0.30+ or Claude Desktop 0.5+

figma-mcp-server running locally or accessible via network

Figma API token configured in server

Limitations

Requires Cursor 0.30+ or Claude Desktop 0.5+ — older versions do not support MCP

Tool invocation depends on LLM understanding of tool schemas — complex queries may not invoke correct tools

No built-in UI for browsing Figma files — all interaction is through text prompts

What makes it unique

vs alternatives

gemini cli integration and command-line tool invocation

Medium confidence

Solves for

Best for

DevOps teams automating design system synchronization in CI/CD

Build systems that need to generate code from Figma designs

Teams using Gemini CLI for multi-tool automation

Requires

Gemini CLI installed and configured

figma-mcp-server running and accessible

Figma API token

Limitations

Requires Gemini CLI to be installed and configured

Tool output is JSON only — requires jq or similar tools for text processing

No streaming responses — large exports may timeout or exceed memory limits

What makes it unique

Exposes MCP tools through Gemini CLI's command-line interface, enabling shell-based automation and CI/CD integration without custom scripting or API client libraries

vs alternatives

More scriptable than GUI-based Figma access, and more flexible than Figma's native webhooks because it allows on-demand queries rather than event-driven updates

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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figma-mcp-server

Capabilities8 decomposed

figma document tree traversal and introspection

component variant enumeration and property extraction

design token extraction and standardization

frame and artboard content export with metadata

mcp tool registration and schema generation

local caching and offline document access

cursor and claude desktop integration

gemini cli integration and command-line tool invocation

Related Artifactssharing capabilities

Figma-Context-MCP

figma-mcp

figma-mcp

@iflow-mcp/figma-mcp

Figma MCP Server

CodeParrot AI: Figma to Code || Design To Code Copilot

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

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Alternatives to figma-mcp-server

Are you the builder of figma-mcp-server?

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Data Sources

figma-mcp-server

Capabilities8 decomposed

figma document tree traversal and introspection

component variant enumeration and property extraction

design token extraction and standardization

frame and artboard content export with metadata

mcp tool registration and schema generation

local caching and offline document access

cursor and claude desktop integration

gemini cli integration and command-line tool invocation

Related Artifactssharing capabilities

Figma-Context-MCP

figma-mcp

figma-mcp

@iflow-mcp/figma-mcp

Figma MCP Server

CodeParrot AI: Figma to Code || Design To Code Copilot

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to figma-mcp-server

Are you the builder of figma-mcp-server?

Get the weekly brief

Data Sources