What can @iflow-mcp/figma-mcp do?

figma document introspection via mcp protocol, mcp tool schema generation from figma api, figma authentication and credential management, figma api request routing and error handling, design file querying with layer filtering and search, component and design token extraction

@iflow-mcp/figma-mcp

MCP ServerFree

ModelContextProtocol server for Figma

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

figma document introspection via mcp protocol

Medium confidence

Exposes Figma API endpoints as MCP tools, allowing LLM agents to query document structure, layers, components, and metadata through a standardized protocol interface. Implements MCP server specification to translate Figma REST API calls into tool definitions that language models can invoke, enabling agents to understand design file hierarchies without direct API knowledge.

Solves for

Query Figma document structure and layer hierarchy programmaticallyRetrieve component definitions and design system metadata from Figma filesInspect design properties (colors, typography, spacing) for automation workflowsEnable LLM agents to reason about design files as part of multi-step workflows

Best for

AI agents and LLM applications that need to read Figma design data

Design automation platforms integrating Figma as a data source

Teams building AI-assisted design-to-code pipelines

Requires

Figma API token (personal access token or OAuth)

MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)

Node.js 16+ runtime environment

Limitations

Read-only introspection — no capability to modify or write changes back to Figma documents

Requires valid Figma API token with appropriate file access permissions

MCP protocol overhead adds latency compared to direct REST API calls

What makes it unique

Bridges Figma REST API and MCP protocol specification, allowing LLM agents to treat Figma documents as queryable tools without requiring agents to understand HTTP semantics or API authentication — the MCP server handles credential management and protocol translation transparently

vs alternatives

Unlike raw Figma API integration, MCP protocol standardization enables drop-in compatibility with any MCP-compatible LLM client (Claude, custom agents) without client-side API binding code

mcp tool schema generation from figma api

Medium confidence

Automatically generates MCP tool definitions that map Figma API endpoints to callable functions with proper parameter schemas, type hints, and descriptions. Uses MCP server specification to define tools with JSON Schema validation, allowing LLM clients to understand available operations and constraints before invocation.

Solves for

Define what Figma operations an LLM agent can perform with proper type safetyEnable LLM clients to discover available Figma queries through tool introspectionValidate agent requests against Figma API constraints before executionGenerate human-readable tool descriptions for agent decision-making

Best for

MCP client developers integrating Figma capabilities into LLM applications

Teams building agentic systems that need design file access

Design tool vendors implementing LLM-native integrations

Requires

MCP server implementation (Node.js)

Figma API documentation or SDK reference

JSON Schema validation library

Limitations

Tool schema generation is static — does not dynamically adapt to Figma API changes without server restart

Schema complexity limited by MCP specification constraints on parameter nesting depth

No automatic schema versioning — breaking API changes require manual schema updates

What makes it unique

Implements MCP tool schema generation specifically for Figma's hierarchical document model, mapping complex nested API responses to flat tool parameters that LLMs can reason about — avoids exposing raw API complexity to agents

vs alternatives

Provides schema-driven tool definition vs manual tool registration, reducing integration boilerplate and enabling automatic validation of agent requests against Figma API constraints

figma authentication and credential management

Medium confidence

Handles Figma API authentication through MCP server configuration, supporting personal access tokens and OAuth flows. Manages credential lifecycle (storage, refresh, expiration) and injects authentication headers into all Figma API requests transparently, isolating clients from credential handling complexity.

Solves for

Securely store and manage Figma API credentials in MCP server configurationAuthenticate all Figma API requests without exposing tokens to LLM clientsSupport multiple authentication methods (personal token, OAuth) for different deployment scenariosHandle token refresh and expiration automatically

Best for

Production deployments requiring secure credential isolation

Multi-tenant systems where each user has different Figma access

Teams deploying MCP servers in shared infrastructure

Requires

Figma personal access token OR OAuth application credentials

Environment variable support or configuration file

MCP server runtime with credential injection capability

Limitations

Credentials must be configured at server startup — no runtime credential rotation without restart

No built-in credential encryption — requires external secret management (environment variables, secret stores)

OAuth flow requires user interaction for initial authorization, not suitable for fully automated systems

What makes it unique

Implements credential management at the MCP server layer rather than client layer, preventing LLM clients from ever handling raw Figma tokens — credentials stay within the server boundary and are injected transparently into API calls

vs alternatives

Centralizes authentication in MCP server vs distributing credentials to multiple clients, reducing attack surface and enabling credential rotation without updating all client configurations

figma api request routing and error handling

Medium confidence

Routes MCP tool invocations to appropriate Figma API endpoints, handles HTTP request/response cycles, and implements error recovery strategies. Translates Figma API errors into MCP-compatible error responses with context, enabling agents to understand failures and retry intelligently.

Solves for

Execute Figma API calls triggered by LLM agent tool invocationsHandle network failures and API errors gracefully with retry logicTranslate Figma API error responses into actionable feedback for agentsManage request rate limiting and backoff strategies

Best for

Agentic systems that need resilient Figma integration

Production workflows where API failures must be handled gracefully

Teams building design automation with retry requirements

Requires

HTTP client library (Node.js built-in or axios/fetch)

Figma API endpoint URLs

Error handling middleware or try-catch patterns

Limitations

Retry logic is server-side only — does not persist failed requests across server restarts

Rate limiting is per-server instance — distributed deployments need external rate limit coordination

Error context is limited to Figma API response — does not include agent-level context

What makes it unique

Implements MCP-aware error handling that translates Figma API errors into MCP error format, preserving error context while conforming to MCP protocol — agents receive structured error information they can reason about

vs alternatives

Provides server-side error handling and retry logic vs client-side handling, reducing complexity for LLM clients and enabling consistent error strategies across all Figma operations

design file querying with layer filtering and search

Medium confidence

Enables agents to query Figma documents with filtering capabilities, searching for specific layers, components, or design elements by name, type, or properties. Implements query translation to Figma API calls, supporting hierarchical traversal of document structure and component library lookups.

Solves for

Find specific design components or layers by name or type in large Figma filesQuery design system components and their properties for code generationRetrieve all instances of a component to understand design consistencySearch for design elements matching specific criteria (color, size, text content)

Best for

Design-to-code automation pipelines that need to locate specific components

Design system auditing tools that verify component usage

AI agents building design-aware applications

Requires

Figma file ID with read access

Figma API token with file access permissions

Document structure already loaded or accessible via Figma API

Limitations

Search is limited to Figma API capabilities — no full-text search across design content

Filtering is performed server-side after fetching full document — inefficient for large files (1000+ layers)

No fuzzy matching — searches require exact or prefix matches

What makes it unique

Implements query-based layer discovery that maps agent search intents to Figma API traversal, abstracting the complexity of recursive document structure navigation — agents query by intent rather than navigating API hierarchies

vs alternatives

Provides semantic search-like interface to Figma documents vs raw API access, enabling agents to express design queries naturally without understanding Figma's hierarchical data model

component and design token extraction

Medium confidence

Extracts component definitions, design tokens (colors, typography, spacing), and style information from Figma files into structured formats. Parses Figma component metadata and applies design system conventions to normalize token names and values for downstream consumption by code generators or design tools.

Solves for

Export design system components and tokens for code generationBuild design-to-code pipelines that consume Figma design tokensAudit design system consistency by extracting all defined tokensGenerate design token documentation from Figma source

Best for

Design systems teams automating token export workflows

Code generation tools that need design token input

Design-to-code platforms building on Figma as source of truth

Requires

Figma file with properly structured components and design tokens

Figma API access to component definitions

Knowledge of Figma naming conventions used in the file

Limitations

Token extraction depends on Figma naming conventions — no automatic normalization for inconsistent naming

Complex nested components may not extract cleanly — requires manual post-processing

Design token values are extracted as-is from Figma — no unit conversion or normalization

What makes it unique

Implements structured extraction of Figma design tokens and components into normalized formats, applying design system conventions to translate Figma's visual representation into machine-readable token definitions — bridges design and code domains

vs alternatives

Provides design-system-aware extraction vs generic API data fetching, enabling downstream tools to consume tokens directly without manual parsing or normalization

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 @iflow-mcp/figma-mcp, ranked by overlap. Discovered automatically through the match graph.

MCP Server28

figma-mcp

ModelContextProtocol server for Figma

figma document tree traversal and introspection via mcpmcp-based tool calling for figma api operationscollaborative design feedback and annotation via mcpfigma-to-code generation via llm prompting

4 shared capabilities

MCP Server46

Figma MCP Server

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

figma api authentication and token managementfigma file structure introspection and traversalmcp tool registration and schema binding

3 shared capabilities

MCP Server41

Figma-Context-MCP

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

configuration management with environment and cli argument supportfigma api client with request handling and error recoverymcp protocol-based tool exposure for ai agents

3 shared capabilities

MCP Server27

figma-mcp

ModelContextProtocol for Figma's REST API

mcp tool registration and schema generationfigma document tree traversal and introspection

2 shared capabilities

MCP Server31

figma-mcp-server

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

figma document tree traversal and introspectionmcp tool registration and schema generation

2 shared capabilities

MCP Server43

cursor-talk-to-figma-mcp

TalkToFigma: MCP integration between AI Agent (Cursor, Claude Code) and Figma, allowing Agentic AI to communicate with Figma for reading designs and modifying them programmatically.

mcp-based agentic tool exposure for figma design operationsfigma plugin lifecycle management and connection state handling

2 shared capabilities

Best For

✓AI agents and LLM applications that need to read Figma design data
✓Design automation platforms integrating Figma as a data source
✓Teams building AI-assisted design-to-code pipelines
✓MCP client developers integrating Figma capabilities into LLM applications
✓Teams building agentic systems that need design file access
✓Design tool vendors implementing LLM-native integrations
✓Production deployments requiring secure credential isolation
✓Multi-tenant systems where each user has different Figma access

Known Limitations

⚠Read-only introspection — no capability to modify or write changes back to Figma documents
⚠Requires valid Figma API token with appropriate file access permissions
⚠MCP protocol overhead adds latency compared to direct REST API calls
⚠Limited to Figma API rate limits (120 requests per minute for standard tier)
⚠Tool schema generation is static — does not dynamically adapt to Figma API changes without server restart
⚠Schema complexity limited by MCP specification constraints on parameter nesting depth

Requirements

Figma API token (personal access token or OAuth)MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)Node.js 16+ runtime environmentNetwork access to Figma API endpointsMCP server implementation (Node.js)Figma API documentation or SDK referenceJSON Schema validation libraryMCP client with tool calling support

Input / Output

Accepts: Figma file ID (string), Node IDs for specific layers/components (string), Query parameters for filtering (JSON object), Figma API endpoint specifications (internal), Parameter definitions and constraints (internal), API token string (environment variable or config), OAuth authorization code (for OAuth flow), Figma workspace/team identifiers, MCP tool invocation with parameters, Figma API endpoint and method, Request headers and authentication, Query string (layer name, component name), Filter criteria (type, properties), Figma file ID, Component or token name patterns, Design system structure definition (optional)

Produces: JSON document structure with layer hierarchy, Component metadata and properties, Design tokens and style definitions, File metadata and version information, MCP tool definition JSON with schema, Parameter validation rules, Tool descriptions and usage examples, Authenticated HTTP headers, Token validity status, Authorization scope information, Figma API response data (JSON), HTTP status codes and error messages, Retry status and backoff information, Matching layer/component objects with metadata, Layer hierarchy and parent references, Component properties and design tokens, JSON/YAML design token definitions, Component metadata with properties, Design system documentation, Code-ready token exports (CSS, TypeScript, etc.)

UnfragileRank

Adoption15%(30% weight)

Quality14%(25% weight)

Ecosystem30%(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

6 capabilities

Visit @iflow-mcp/figma-mcp→

Package Details

npm

Registry

0.1.4

Version

Weekly Downloads

About

ModelContextProtocol server for Figma

Alternatives to @iflow-mcp/figma-mcp

IntelliCode50Extension

AI-assisted development

Compare →

GitHub Copilot Chat53Extension

AI chat features powered by Copilot

Compare →

GitHub Copilot52Extension

Your AI pair programmer

Compare →

Claude Code for VS Code52Extension

Claude Code for VS Code: Harness the power of Claude Code without leaving your IDE

Compare →

Are you the builder of @iflow-mcp/figma-mcp?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

mcp registry

Looking for something else?

Search →

Capabilities6 decomposed

figma document introspection via mcp protocol

Medium confidence

Solves for

Best for

AI agents and LLM applications that need to read Figma design data

Design automation platforms integrating Figma as a data source

Teams building AI-assisted design-to-code pipelines

Requires

Figma API token (personal access token or OAuth)

MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)

Node.js 16+ runtime environment

Limitations

Read-only introspection — no capability to modify or write changes back to Figma documents

Requires valid Figma API token with appropriate file access permissions

MCP protocol overhead adds latency compared to direct REST API calls

What makes it unique

vs alternatives

Unlike raw Figma API integration, MCP protocol standardization enables drop-in compatibility with any MCP-compatible LLM client (Claude, custom agents) without client-side API binding code

mcp tool schema generation from figma api

Medium confidence

Solves for

Best for

MCP client developers integrating Figma capabilities into LLM applications

Teams building agentic systems that need design file access

Design tool vendors implementing LLM-native integrations

Requires

MCP server implementation (Node.js)

Figma API documentation or SDK reference

JSON Schema validation library

Limitations

Tool schema generation is static — does not dynamically adapt to Figma API changes without server restart

Schema complexity limited by MCP specification constraints on parameter nesting depth

No automatic schema versioning — breaking API changes require manual schema updates

What makes it unique

vs alternatives

Provides schema-driven tool definition vs manual tool registration, reducing integration boilerplate and enabling automatic validation of agent requests against Figma API constraints

figma authentication and credential management

Medium confidence

Solves for

Best for

Production deployments requiring secure credential isolation

Multi-tenant systems where each user has different Figma access

Teams deploying MCP servers in shared infrastructure

Requires

Figma personal access token OR OAuth application credentials

Environment variable support or configuration file

MCP server runtime with credential injection capability

Limitations

Credentials must be configured at server startup — no runtime credential rotation without restart

No built-in credential encryption — requires external secret management (environment variables, secret stores)

OAuth flow requires user interaction for initial authorization, not suitable for fully automated systems

What makes it unique

vs alternatives

Centralizes authentication in MCP server vs distributing credentials to multiple clients, reducing attack surface and enabling credential rotation without updating all client configurations

figma api request routing and error handling

Medium confidence

Solves for

Best for

Agentic systems that need resilient Figma integration

Production workflows where API failures must be handled gracefully

Teams building design automation with retry requirements

Requires

HTTP client library (Node.js built-in or axios/fetch)

Figma API endpoint URLs

Error handling middleware or try-catch patterns

Limitations

Retry logic is server-side only — does not persist failed requests across server restarts

Rate limiting is per-server instance — distributed deployments need external rate limit coordination

Error context is limited to Figma API response — does not include agent-level context

What makes it unique

vs alternatives

Provides server-side error handling and retry logic vs client-side handling, reducing complexity for LLM clients and enabling consistent error strategies across all Figma operations

design file querying with layer filtering and search

Medium confidence

Solves for

Best for

Design-to-code automation pipelines that need to locate specific components

Design system auditing tools that verify component usage

AI agents building design-aware applications

Requires

Figma file ID with read access

Figma API token with file access permissions

Document structure already loaded or accessible via Figma API

Limitations

Search is limited to Figma API capabilities — no full-text search across design content

Filtering is performed server-side after fetching full document — inefficient for large files (1000+ layers)

No fuzzy matching — searches require exact or prefix matches

What makes it unique

vs alternatives

Provides semantic search-like interface to Figma documents vs raw API access, enabling agents to express design queries naturally without understanding Figma's hierarchical data model

component and design token extraction

Medium confidence

Solves for

Best for

Design systems teams automating token export workflows

Code generation tools that need design token input

Design-to-code platforms building on Figma as source of truth

Requires

Figma file with properly structured components and design tokens

Figma API access to component definitions

Knowledge of Figma naming conventions used in the file

Limitations

Token extraction depends on Figma naming conventions — no automatic normalization for inconsistent naming

Complex nested components may not extract cleanly — requires manual post-processing

Design token values are extracted as-is from Figma — no unit conversion or normalization

What makes it unique

vs alternatives

Provides design-system-aware extraction vs generic API data fetching, enabling downstream tools to consume tokens directly without manual parsing or normalization

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

Alternatives to @iflow-mcp/figma-mcp

IntelliCode50Extension

AI-assisted development

Compare →

GitHub Copilot Chat53Extension

AI chat features powered by Copilot

Compare →

GitHub Copilot52Extension

Your AI pair programmer

Compare →

Claude Code for VS Code52Extension

Claude Code for VS Code: Harness the power of Claude Code without leaving your IDE

Compare →

@iflow-mcp/figma-mcp

Capabilities6 decomposed

figma document introspection via mcp protocol

mcp tool schema generation from figma api

figma authentication and credential management

figma api request routing and error handling

design file querying with layer filtering and search

component and design token extraction

Related Artifactssharing capabilities

figma-mcp

Figma MCP Server

Figma-Context-MCP

figma-mcp

figma-mcp-server

cursor-talk-to-figma-mcp

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @iflow-mcp/figma-mcp

Are you the builder of @iflow-mcp/figma-mcp?

Get the weekly brief

Data Sources

@iflow-mcp/figma-mcp

Capabilities6 decomposed

figma document introspection via mcp protocol

mcp tool schema generation from figma api

figma authentication and credential management

figma api request routing and error handling

design file querying with layer filtering and search

component and design token extraction

Related Artifactssharing capabilities

figma-mcp

Figma MCP Server

Figma-Context-MCP

figma-mcp

figma-mcp-server

cursor-talk-to-figma-mcp

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @iflow-mcp/figma-mcp

Are you the builder of @iflow-mcp/figma-mcp?

Get the weekly brief

Data Sources