Understanding Model Context Protocol (MCP): Architecture, Usage, and Limitations

Model Context Protocol (MCP) is a standardized protocol designed to bridge AI models with external resources, allowing real‑time data and tool access beyond the static training dataset.

How MCP Works

MCP follows a client‑server architecture: the AI application acts as a client that sends requests, while resource servers (e.g., Google Drive, Slack, Figma) respond with the required data or functionality.

Significance

By breaking the isolation of AI models, MCP enables retrieval of up‑to‑date information, invocation of external tools, and more flexible operation in dynamic environments.

Example Use‑Case

To fetch the latest document from Google Drive, a developer can set up an MCP server that connects to Drive, the AI client requests the document, and the server returns the content for analysis or generation.

Resources and Trends

Numerous articles and repositories list MCP servers and implementations. Major vendors are adopting MCP, and the ecosystem is expanding with integrations such as Vercel AI SDK, Cloudflare, and Baidu Maps.

Getting Started with a Figma MCP Server

Clone the repository.

Install dependencies with pnpm install .

Copy .env.example to .env and add your Figma API token (read‑only permission is sufficient).

Start the server using pnpm run dev , optionally passing command‑line flags such as --port or --figma-api-key . Configuration can also be supplied via environment variables ( FIGMA_API_KEY , PORT ).

Connecting Cursor to the MCP Server

After the server is running, configure Cursor to connect to the MCP endpoint. Verify the connection by checking for a green indicator in the UI.

Using the Agent Mode

With the latest Cursor version, the Composer has been replaced by an Agent mode. Once connected, you can drag a Figma file link into the workspace and trigger the get_file tool.

Inspecting Responses

Run pnpm inspect to launch the @modelcontextprotocol/inspector web UI, which allows you to invoke tools and view responses.

Available MCP Tools

Get Figma Data : Retrieve information about a Figma file or specific node. Parameters include fileKey (required), nodeId (optional), and depth (optional).

Download Figma Images (in development) : Download SVG/PNG images from specified nodes. Required parameters are fileKey , nodes (array of node IDs), and localPath for storage.

Current Limitations

Complex designs, dynamic interactions, and context management remain challenging for MCP. Inconsistent naming conventions and unstructured layer hierarchies reduce its effectiveness.

Importance of Design Standards in the AI Era

Consistent naming and component standards improve team collaboration, enable tools like MCP to function optimally, and simplify maintenance.

Low‑Code + AI Generated JSON

Combining low‑code platforms with AI to generate structured JSON from design files can improve accuracy, automate workflows, and free designers to focus on creativity.

Future Outlook

Short‑Term Improvements : Enforce lightweight design guidelines to maximize MCP benefits.

Exploring New Tools : Pilot low‑code platforms (e.g., Webflow, Bubble) integrated with AI for more practical solutions.

Long‑Term Direction : Track the evolution of low‑code + AI to shape future design tool strategies.

The author invites further discussion and encourages readers to explore related articles on the original platform.