The Model Context Protocol (MCP) is an open-standard communication protocol designed to provide AI models with a structured, secure way to interact with external tools and datasets. Traditionally, an agent’s knowledge is confined to its training data; MCP breaks this silo by defining a standard for "servers" that expose specific capabilities, or "tools", to a client (the LLM). In the context of modern software development, this allows an engineer or designer to use an AI agent as a collaborative partner that can inspect codebases, query databases, or, as explored here, manipulate design files in real-time.

A significant challenge in the design-to-code pipeline is the static nature of most design integrations. While official integrations often allow an agent to "see" a design to generate code, they rarely allow the agent to "write" back to the design environment. This limitation stems from the architectural constraints of web-based design platforms like Figma. The Community Figma MCP Server, introduced by technical leaders in the space, addresses this gap by providing a mechanism for agents to perform layout adjustments, create components, and manage design systems directly within the Figma canvas[^1].

Bridging the Gap: From Read-Only to Generative Design

Most professional design tools prioritize security and stability, which often results in APIs that are heavily restricted. Figma, for example, offers a robust REST API, but it is primarily read-only. This means that while an MCP server could easily fetch the properties of a frame or a layer, it cannot use a simple HTTP request to move a button or change a hex code. The only way to modify a Figma document programmatically is through its Plugin API, which runs directly in the user’s browser environment.

The Community Figma MCP Server solves this by moving beyond a simple API wrapper. It acknowledges that a direct connection between an LLM and Figma’s internal document object model (DOM) requires an active execution context. By leveraging the Plugin API, the server can access the full suite of Figma’s editing capabilities, including Auto Layout, component properties, and coordinate systems. This allows an AI agent to act like a human designer, issuing commands that result in the creation of complex UI structures, such as login forms or navigation bars, from scratch based on a text prompt[^1].

The Generative Feedback Loop

Implementing a generative design tool requires more than just "drawing" shapes; it requires an understanding of design best practices. A critical aspect of this MCP implementation is the use of structured "memory" files or system prompts that guide the agent’s behavior. For instance, without specific constraints, an agent might overlap elements at the same (0,0) coordinates. By providing the agent with a context-aware environment, it can be instructed to:

• Use Auto Layout for all new frames to ensure responsiveness.
• Organize components on a dedicated "Components" page.
• Apply consistent spacing and alignment based on an existing design system.

This approach transforms the LLM from a simple script executor into a design assistant that understands intent. If a user is dissatisfied with a layout, they can simply prompt the agent to "align these buttons to the left" or "swap the primary and secondary actions." The MCP server then translates these high-level intentions into a series of tool calls that modify the Figma document in real-time.

Behind the Scenes: WebSocket Architecture and Tool Wiring

The architecture of the Community Figma MCP Server is designed to bypass the sandbox limitations of Figma plugins. Because a plugin cannot act as a standalone server, a three-tier system is employed:

1. The MCP Server: Acts as the entry point for the AI agent (e.g., Claude or Opus). It exposes a set of 23+ tools that define actions like createRect, setPadding, or addComponentInstance.
2. The WebSocket Server: Serves as a medium or message broker. It maintains a persistent connection between the MCP server and the active Figma plugin.
3. The Figma Plugin: Runs within the Figma client. It listens for messages via the WebSocket, executes the requested design changes using the Plugin API, and sends a confirmation or result back through the chain.

When an agent initiates a tool request, the MCP server places the call into a queue. This is vital for managing the asynchronous nature of design updates. Once the plugin completes the task, it returns the updated context to the WebSocket server, which then resolves the original tool call in the MCP environment. This ensures that the agent always has an accurate "view" of the document state before making the next design decision[^1].

// Conceptual example of a tool call handling design logic
async function handleCreateComponent(name: string, properties: any) {
const message = {
type: 'EXECUTE_ACTION',
action: 'CREATE_COMPONENT',
payload: { name, properties }
};

// Sending the message through the WebSocket bridge
websocket.send(JSON.stringify(message));

// Wait for the plugin to confirm execution
return await waitForPluginResponse();
}

My Thoughts

The Community Figma MCP Server represents a pivotal moment in the evolution of "AI-assisted" vs. "AI-automated" workflows. While tools like Figma’s own "Make Design" features provide one-off generation, the MCP approach allows for iterative, conversational editing. This is particularly valuable for developers who may lack deep design expertise but need to scaffold functional prototypes quickly.

However, there are inherent limitations. The reliance on a custom WebSocket bridge adds a layer of deployment complexity, users must run both the MCP server and a local plugin. Furthermore, LLMs still occasionally struggle with the spatial reasoning required for pixel-perfect layouts without very detailed "system instructions." As the protocol matures, I expect we will see tighter integrations where these "bridge" architectures become more transparent to the end-user, potentially through official support for bidirectional API access in design platforms.

Acknowledgements

I would like to thank Anton Tishchenko, CTO at EXDST, for his detailed walkthrough of this implementation during the "Community Figma MCP Server" talk hosted by the MCP Developers Summit. His insights into the architectural workarounds required for the Figma environment have been invaluable to the community. Gratitude is also extended to the broader MCP and AI research community for continuously pushing the boundaries of tool-use standards.