Figma MCP Server

The Figma MCP server bridges the gap between AI assistants and design workflows by exposing Figma files, components, and assets through a unified Model Context Protocol interface. It allows AI agents to programmatically explore design hierarchies, retrieve structured data about pages and components, and export visual assets in multiple formats—all without leaving the conversational context. This eliminates the need for manual API calls or custom integrations, enabling designers and developers to query and manipulate Figma content directly from AI-powered tools.

What Problem Does It Solve?

Design files in Figma are rich, nested structures that typically require the Figma REST API to access. Developers building AI assistants often need to fetch file metadata, navigate component trees, or pull out images for downstream processing (e.g., generating documentation, creating style guides, or feeding assets into other ML pipelines). The Figma MCP server abstracts these operations behind a consistent protocol, allowing an AI client to treat design data like any other resource. This removes the friction of authentication handling, pagination, and format conversion, making it trivial to integrate design assets into AI workflows.

Core Functionality & Value

• File and Node Exploration: and return JSON representations of a file’s structure or specific nodes, with optional depth control to keep responses within token limits.
• URL Parsing: extracts file keys from standard Figma URLs, streamlining the onboarding of shared links.
• Asset Export: can generate PNG, JPG, SVG, or PDF snapshots of any node. The exported images become MCP resources with base64-encoded content, immediately usable by the AI assistant for rendering or further analysis.
• Authentication Testing: confirms that the provided Figma token is valid, simplifying error handling in client code.
• Resource Management: Exported images are available through resource URIs (), allowing AI assistants to list, read, or cache assets as needed.

These capabilities give developers a single entry point for all design-related queries, reducing boilerplate and enabling richer interactions such as “Show me the color palette used on page 3” or “Export a PNG of component X for use in documentation.”

Use Cases & Real-World Scenarios

1. Automated Design Documentation – An AI assistant can walk through a Figma file, extract component names and properties, and generate Markdown or HTML style guides.
2. Rapid Prototyping – Designers can ask the assistant to pull a specific component and export it in the desired format for quick iteration or handoff.
3. Data-Driven Design Audits – By querying node hierarchies and exporting images, an AI can analyze consistency, color usage, or layout across a file.
4. CI/CD Integration – Tests can fetch the latest design assets, compare them against expected snapshots, and report regressions automatically.
5. Chatbot Assistance – A conversational AI can answer questions about a shared Figma link, providing real-time previews or linking directly to the exported images.

Integration with AI Workflows

The server’s MCP tools integrate seamlessly into any AI assistant that supports the Model Context Protocol. A typical flow involves:

1. User Input – The assistant receives a Figma URL or node reference from the user.
2. URL Parsing – extracts the file key and optional node ID.
3. Data Retrieval – or fetches the relevant JSON structure, optionally limiting depth to stay within token budgets.
4. Asset Export – generates the required visual asset, which the assistant can embed directly in responses.
5. Resource Consumption – The exported image, being an MCP resource, can be read as base64 and rendered inline or stored for future use.

Because all interactions are defined by MCP, developers can compose complex design queries without writing custom HTTP clients or handling pagination logic. The server’s depth parameter also ensures that AI agents can tailor response size to fit their context window, preventing truncation or excessive token usage.

Unique Advantages

• Standardized Interface – By adhering to MCP, the Figma server fits naturally into any ecosystem that already consumes or produces MCP-compatible data.
• Depth Control – The ability to specify how deep the traversal should go gives AI assistants fine-grained control over payload size, a critical feature for large design files.
• Built-in Resource Handling – Exported images are automatically exposed as resources