MCP

MCP is an open standard Anthropic released in November 2024. The motivation is one sentence: each model and vendor had its own way of wiring up tools, and integration costs were exploding. OpenAI Function Calling, Anthropic Tool Use and Google Function Calling were doing similar work in different shapes. MCP proposes standardising the tool side instead of the model side — build one MCP server and Claude, ChatGPT, Cursor and other clients can all reach the same data the same way.

The architecture is client-server. An MCP server exposes tools, resources and prompts; an MCP client like Claude Desktop or Cursor connects to that server and surfaces the capabilities to the LLM. Communication runs over JSON-RPC, with stdio, HTTP and SSE all supported as transports. The net effect is that 'expose your data and functions through MCP once, and every MCP-aware LLM client integrates at the same time'.

From a Villion and GEO angle, MCP is more than a developer convenience. If AI Overviews and ChatGPT Search are the content surfaces, MCP is the data and tool surface. Expose your catalog, docs and customer data through an MCP server and Claude, ChatGPT and other agents can call your data directly when composing answers. That is a different layer of GEO from 'showing up in search results' — making your tools the ones an agent naturally reaches for becomes a new kind of marketing asset.

It helps to position MCP against the other standards. MCP targets the LLM-to-tool link. A2A targets the agent-to-agent link. UCP targets the agent-to-commerce payment link. They live at different layers more than they compete, but the boundaries are not crisp and there is real politics involved, so the survivors will probably not be clear until late 2026. At adoption time, do not bet hard on one standard — focus on making your data and functions clean enough that any of them can wrap them.

A common misread in Korea is that MCP is purely a developer concern. In practice, marketing and operations need a seat at the table because the very first question — 'which data do we expose to which external LLM?' — is a policy question about permissions, audit logging and sensitive-data masking. The security model is also still maturing, so day-one adopters should bake in least-privilege scopes, short-lived tokens and per-request audit logs. For GEO, MCP is both an opportunity and a data-governance stress test.