Figma 파일 구조를 분석하기 위한 MCP(Model Context Protocol) 서버입니다.
Figma 파일을 분석하여 노드 계층 구조 추출
REST API와 MCP 프로토콜을 모두 지원합니다
구성 가능한 노드 트리 깊이
Related MCP server: Figma MCP
노드.js 16+
npm 또는 yarn
Figma API 키
지엑스피1
git clone https://github.com/yourusername/mcp-figma.git
cd mcp-figma
npm install
npm run build예제 환경 파일을 복사하고 Figma API 키를 추가하세요.
cp .env.example .env그런 다음 .env 파일을 편집하고 Figma API 키를 추가하세요.
FIGMA_API_KEY=your_figma_api_key_hereFigma 계정 설정에서 Figma API 키를 얻을 수 있습니다: https://www.figma.com/developers/api#access-tokens
서버를 시작합니다:
npm start이렇게 하면 포트 3000(또는 .env 파일에 지정된 포트)에서 Express 서버가 시작됩니다.
GET /health - 상태 확인 엔드포인트
GET /openapi.json - OpenAPI 사양
GET /mcp.json - MCP 매니페스트
POST /analyze - Figma 파일 분석
분석 엔드포인트에 대한 요청 예:
curl -X POST http://localhost:3000/analyze \
-H "Content-Type: application/json" \
-d '{"figmaUrl": "https://www.figma.com/file/FILE_ID/PROJECT_NAME?node-id=NODE_ID", "depth": 2}'LLM은 MCP 프로토콜을 통해 서버를 직접 사용할 수 있습니다.
figma-mcp-server --cli또는 소스에서 실행하는 경우:
npm run start -- --cli간단한 클라이언트 예제가 포함되어 있습니다. 사용 방법은 다음과 같습니다.
node client-example.js https://www.figma.com/file/FILE_ID/PROJECT_NAME?node-id=NODE_ID 2마지막 매개변수는 선택적 깊이 매개변수입니다.
npm run devnpm test컨테이너화된 배포를 위해 Dockerfile이 제공됩니다.
docker build -t figma-mcp-server .
docker run -p 3000:3000 --env-file .env figma-mcp-serverMIT
Analyze a Figma file structure to understand its nodes and hierarchy
| Name | Required | Description | Default |
|---|---|---|---|
| figmaUrl | Yes | The URL of the Figma file to analyze | |
| depth | No | Optional depth parameter to limit the node tree depth |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes structure but doesn't mention whether it's read-only, requires authentication, has rate limits, returns specific formats, or handles errors. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose. It avoids redundancy and is appropriately sized for a simple tool, though it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the analysis returns, how nodes are structured, or any behavioral traits. For a tool that likely outputs complex hierarchical data, this lack of context makes it inadequate for full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so parameters are well-documented in the schema. The description adds minimal value beyond implying analysis of 'nodes and hierarchy', which relates to the depth parameter. It doesn't provide additional syntax or format details, so baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('analyze') and resource ('Figma file structure'), specifying the goal is to 'understand its nodes and hierarchy'. It's specific about what the tool does, though without sibling tools, differentiation isn't needed. However, it's slightly vague on the exact output or analysis type beyond 'understand'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or constraints. It mentions analyzing file structure but doesn't specify use cases like design review, extraction, or debugging. With no sibling tools, this is less critical, but still lacks operational context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
analyze_figma_fileScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct by default, as there are no other tools to confuse it with.
The single tool follows a clear verb_noun pattern (analyze_figma_file), and with only one tool, consistency is inherently perfect. There are no other tools to deviate from this pattern.
A single tool is too few for a server named 'Figma MCP Server,' which suggests a broader domain like Figma file management. This minimal set likely leaves significant gaps in functionality, such as creating, updating, or listing files, making it feel incomplete for the implied scope.
The tool surface is severely incomplete for a Figma server. It only provides analysis of file structure, missing essential operations like retrieving files, creating or editing designs, managing comments, or accessing other Figma resources, which are core to the domain.
The Figma MCP server brings Figma design context directly into your AI workflow.
Geometry and CAD file metadata extraction for STL, OBJ, PLY, PCD, LAS/LAZ, glTF/GLB.
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Access and maintain design system docs, tokens, components, skills, and contexts across any project.
