Model Context Protocol (MCP), Explained for Marketers

The Model Context Protocol (MCP) is an open standard that lets an AI assistant connect to outside systems through one common interface. A server exposes a list of tools — named actions with defined inputs. A client — the AI app you already use — discovers that list and calls the tools when a conversation needs them. Anthropic published the protocol in November 2024, and it has since become the default way assistants reach live data, including ad accounts.

The shorthand that stuck: MCP is USB for AI tools. One port, any device. Before USB, every peripheral shipped its own cable and its own card. Before MCP, every AI integration was its own project.

How MCP works

MCP has three words that matter, and they map onto things you already know:

• Server — a program that wraps a real system (an API, a database, a file store) and exposes it as tools. A Google Ads MCP server wraps the Google Ads API.
• Client — the AI application: Claude, ChatGPT, GitHub Copilot, Cursor, Gemini CLI. The client reads what tools a server offers and decides, mid-conversation, when to call one.
• Tools — the individual actions, each with a name, a description and typed inputs. list_accounts. run_gaql. add_negative_keywords. The tool list is the complete statement of what the AI can do through that server.

A conversation flows like this: you ask a question; the model recognises it needs live data; the client sends a tool request to the server; the server executes it against the real system and returns structured results; the model reads them and answers. Writes work the same way, with one addition — most clients show you the exact call and wait for approval before sending it.

The problem it replaced

Before MCP, connecting several assistants to several systems meant a custom integration per pair — a ChatGPT plugin here, a Claude-specific build there, each with its own auth, its own format, its own maintenance. Nobody built them all, so most tools connected to nothing. MCP collapses the grid: build one server, and every compliant client can use it. The integration decision and the assistant decision become independent.

What MCP changes for Google Ads work

For Google Ads specifically, MCP is the difference between an AI that talks about marketing and an AI that has read your search terms report. A connected assistant pulls real spend, real queries, real change history — and, with approval, makes real changes: campaigns, budgets, negative keywords, ad copy.

Because the protocol is shared, the connector is portable. The same hosted Google Ads connector address works in Claude today and in ChatGPT tomorrow — switching assistants no longer means rebuilding the integration. And because capability is defined by the server's tool list, you can read exactly what the AI is allowed to do before you connect anything. A server with no delete tools cannot delete, whatever the model intends.

Who maintains the standard, and who has adopted it

MCP is open: the specification and reference implementations are public, and contributions now come from across the industry rather than one vendor. Anthropic originated it; adoption is what made it matter. The major AI clients — Claude (desktop and web), Claude Code, ChatGPT, GitHub Copilot, Cursor, Gemini CLI and a growing list — speak it, mostly over the streamable HTTP transport that remote connectors use. The current tested list for ads work lives on the client compatibility page.

For a marketer, the adoption curve carries one practical message: MCP knowledge is not a bet on a single AI vendor. Learn what a server is, what a client is, and how to judge a tool list, and that knowledge travels with you across every assistant you use next.