Figma-Context MCP Server

What does “Figma-Context” MCP Server do?

The Figma-Context MCP Server is a tool designed to bridge the gap between AI coding agents and Figma design layouts. By exposing Figma layout data through the Model Context Protocol (MCP), it empowers AI assistants—such as those used in platforms like Cursor—to fetch, analyze, and reason about Figma files directly during development workflows. This enables scenarios where AI can assist with UI implementation, code generation, component extraction, or design-to-code translation by retrieving up-to-date layout information from Figma projects. The server acts as an intermediary, facilitating secure and structured API interactions with Figma and making the data accessible as context for LLMs and developers.

List of Prompts

No explicit prompt templates are listed in the repository or its documentation.

List of Resources

No explicit MCP resources are described in the repository or its documentation.

List of Tools

No explicit list of tools is found (e.g., from server.py or code listings). Details about tool endpoints or functions are not present in the available documentation.

Use Cases of this MCP Server

• UI Implementation Assistance: Allows AI agents to access Figma layouts and support developers in translating designs into code, ensuring pixel-perfect results.
• Component Extraction: Enables automated identification and extraction of reusable components from Figma files, accelerating frontend development.
• Design Review Automation: Provides context for AI to review code against Figma designs, catching inconsistencies early.
• Documentation Generation: AI can generate documentation for UI elements directly from Figma layouts, improving team communication.
• Rapid Prototyping: Facilitates the creation of code prototypes based on live Figma files, speeding up iteration cycles.

How to set it up

Windsurf

1. Ensure you have Node.js installed.
2. Locate your Windsurf configuration file.
3. Add the Figma-Context MCP Server using a JSON snippet in the mcpServers section:

   {
     "figma-context": {
       "command": "npx",
       "args": ["@GLips/figma-context-mcp-server@latest"]
     }
   }
   

4. Save your configuration and restart Windsurf.
5. Verify the server is running via Windsurf logs or UI.

Securing API Keys

Store your Figma API key in an environment variable and reference it in your configuration:

{
  "figma-context": {
    "env": {
      "FIGMA_API_KEY": "${FIGMA_API_KEY}"
    },
    "inputs": {
      "figmaApiKey": "${FIGMA_API_KEY}"
    }
  }
}

Claude

1. Install Node.js.
2. Open Claude’s configuration panel or file.
3. Insert the Figma-Context MCP Server in your mcpServers array:

   {
     "figma-context": {
       "command": "npx",
       "args": ["@GLips/figma-context-mcp-server@latest"]
     }
   }
   

4. Save and restart Claude.
5. Confirm connection by checking server status in Claude.

Cursor

1. Make sure Node.js is installed.
2. Edit your Cursor settings or configuration file.
3. Add the following to the mcpServers property:

   {
     "figma-context": {
       "command": "npx",
       "args": ["@GLips/figma-context-mcp-server@latest"]
     }
   }
   

4. Save changes and restart Cursor.
5. Check logs or UI for successful server startup.

Cline

1. Install Node.js if not already present.
2. Access your Cline configuration.
3. Add the Figma-Context MCP Server with:

   {
     "figma-context": {
       "command": "npx",
       "args": ["@GLips/figma-context-mcp-server@latest"]
     }
   }
   

4. Save and relaunch Cline.
5. Validate integration via Cline diagnostics.

Note: Always secure your Figma API key using environment variables as shown in the Windsurf section above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "figma-context": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "figma-context" to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

This MCP implementation provides a clear overview and setup instructions, but lacks detailed documentation on prompts, resources, and specific tool endpoints, which limits its out-of-the-box discoverability for custom workflows.

MCP Score

Rating summary:
Based on the above information, I would rate this MCP server a 4/10. It provides a good overview, is widely used (many stars/forks), and has a clear open-source license, but lacks detailed documentation about its MCP tools, resources, and prompts, which is crucial for advanced MCP integration and developer adoption.