Double Click: What Does MCP Mean for Agentic AI? | Figma Blog

MCP is emerging as a common interoperability layer between AI assistants and external tools or data sources. By allowing models to discover and invoke tools during a conversation, it can make agentic systems faster, more scalable, and less dependent on bespoke integrations. The growing ecosystem—including Figma’s own MCP server—suggests MCP could become foundational infrastructure for the agentic web.

What MCP Is

• Anthropic introduced the Model Context Protocol in November, 2024; support from OpenAI helped drive widespread adoption.
• MCP standardizes how assistants such as Claude, Copilot, and Cursor communicate with tools and data.
• Developers can avoid building separate custom integrations for every AI assistant and service.
• The protocol is compared to:
  • USB-C, as a universal connection for AI applications.
  • HTTP, because it is lightweight, composable, interoperable, and largely unconcerned with the payload.
• Microsoft CTO Kevin Scott described MCP as a potential backbone for agent communication and evolution.

MCP and Agentic AI

• MCP gives large language models real-time access to tools and information.
• This enables AI agents to act on a user’s behalf rather than merely generate text or code.
• The number of available MCP servers is growing rapidly.
• Figma created an MCP server that connects design information directly to developer workflows, supporting design-informed code generation.

MCP as an Accelerant

• Traditional API-based workflows often require an LLM to generate code and execute it whenever it needs to interact with a tool.
• With MCP, the model can recognize available tools and invoke them directly during a conversation.
• This reduces friction and improves speed, efficiency, and scalability.
• The resulting experience makes complex actions feel more immediate—users can increasingly “just do things.”

Early Examples of MCP Use

• An automated AI travel agency used four agents across Google Maps, Airbnb, Google Calendar, and Weather.
• A Blender MCP server generated a 3D scene of a low-poly dragon guarding treasure from a few natural-language instructions.
• Y Combinator hosted a large MCP hackathon, demonstrating the rapid growth of experimentation around the protocol.

MCP’s main promise is not simply connecting AI to APIs, but creating a shared, extensible standard that lets agents use many tools dynamically. Its long-term impact will depend on how reliably and responsibly these increasingly capable systems operate.