Before the Model Context Protocol (MCP), connecting an AI model to an external tool like Figma required building custom, one-off integrations. Each new tool or data source meant writing new code from scratch, creating a tangled web of brittle connections that were difficult to maintain and scale.

MCP introduces a standardized way for AI models and external tools to communicate. Think of it like a universal adapter. Instead of needing a different plug for every device, you have one standard that just works. This open protocol, introduced by Anthropic, replaces bespoke API wrappers with a reusable, secure, and discoverable framework.

MCP's architecture relies on three distinct components working in concert. Understanding their roles is key to grasping how the protocol functions.

• The MCP Client is the AI model or agent. It initiates requests for information or actions. For example, the LLM might ask,

• The Host Application is the environment where the AI runs, like Claude Desktop or an IDE like Cursor. It acts as a trusted intermediary, discovering available MCP servers and routing the client's requests to the correct destination. The host manages the connection, ensuring that the AI model doesn't need to know the specific network address or authentication details for each tool.

• The MCP Server is a lightweight service that exposes a specific tool, database, or API. It's the bridge to the external resource. An MCP server for Figma would translate the AI's standardized request into a specific, authenticated call to Figma's REST API, then format the response back into the standard MCP structure.

The MCP approach fundamentally changes how we build tool-using AI. Instead of every application writing its own custom code to talk to Figma, developers can build one reusable MCP server for Figma. Any MCP-compatible host application can then discover and use it instantly.

This model shifts the focus from writing brittle, application-specific glue code to creating robust, reusable tools. An engineer can build a Figma MCP server once, and it can be leveraged by a chatbot, a code editor, an automation script, or any other AI application that supports the protocol.

The protocol defines a clear handshake and communication flow. When a host application starts, it scans the local environment for available MCP servers. Each server advertises its capabilities, essentially telling the host, "I can connect to Figma and perform these specific actions." This is the discovery phase.

When the AI client makes a request, the host application routes it to the appropriate server. The communication itself happens over a defined transport mechanism, typically a local WebSocket or a Unix domain socket. This ensures fast, secure, and low-latency communication between the components running on your machine. The MCP server then makes the necessary outbound call to Figma's cloud API over HTTPS.

The protocol is more than just a request-response pattern. It supports bi-directional communication, allowing the server to push updates to the client. For example, if a collaborator makes a change in the Figma file, the MCP server could proactively notify the AI model, enabling real-time, context-aware interactions.

This standardized architecture paves the way for a more interoperable and powerful ecosystem of AI tools.