What Is MCP? The Protocol That Lets AI Run Your Ad Accounts

The problem MCP solves

AI assistants are good at reasoning, summarizing, and writing. But on their own, they cannot do anything in the real world. They cannot check your ad spend, pause a campaign, or pull a performance report. They are brains without hands.

To give an AI assistant hands, it needs a structured way to reach the outside world. It needs to discover what actions are available, understand what each one needs from it, take the action, and read back the result. For years every company solved this differently, with its own plugins and its own glue code, none of which worked together.

The Model Context Protocol (MCP) is the open standard that fixes this. It is a common language that lets any AI app talk to any external tool. Once a service speaks MCP, your assistant can use it without anyone writing custom integration code.

For advertising, that is the whole game. MCP is what lets you point Claude or ChatGPT at your Meta, Google, and TikTok ad accounts and just say what you want.

What MCP is, in one sentence

MCP is a universal adapter between AI assistants and the tools they use. Think of it like a USB port. Before USB, every device needed its own cable and its own driver. After USB, anything that fits the port just works. MCP is the same idea for AI: it is the port, and a service like Xylo is the thing you plug in.

You do not need to understand the wiring to benefit from it, the same way you do not think about USB voltage when you charge your phone. But a quick look under the hood makes the rest of this clearer.

How it works, without the jargon

MCP has two sides that talk to each other.

The MCP app is the thing you chat with: Claude, ChatGPT, Cursor, Claude Code, or Codex. When you connect a service to it, the app asks, "what can you do?" and gets back a menu of available actions.

The MCP server is the service on the other end. It publishes that menu of actions and does the real work when one is called. Xylo is an MCP server. Its menu includes actions like "list campaigns," "pause this ad," "build a lookalike audience," and "pull a cross-platform report." When your assistant calls one, Xylo talks to Meta, Google, or TikTok on your behalf and hands back the result.

That is the entire model. You talk to the app. The app picks the right action from the menu. The server runs it. You get an answer.

The three things an MCP server can offer

Every MCP server, Xylo included, can expose three kinds of things. You will never configure these yourself, but they explain what your assistant is reaching for behind the scenes.

Tools

Tools are the actions. Each one has a plain-English description so the AI knows when to use it, and a defined set of inputs so it knows what to provide. "Pause this campaign" is a tool. "Create a lookalike audience" is a tool. Xylo packs 300 and more ad operations across Meta, Google, and TikTok into 25 tools that load instantly in any AI client, covering essentially every surface you would find in Ads Manager, plus AI creative analysis and cross-platform reporting.

Resources

Resources are background information the AI can read for context, such as account settings, account limits, or a catalog of targeting options. They inform decisions rather than change anything.

Prompts

Prompts are reusable starting points for common jobs, like a weekly performance review or a budget check. They give the AI a head start on workflows you run again and again.

Why this beats letting AI guess at an API

A fair question: why not let the AI just call a regular web API directly?

Because without MCP, the AI is guessing. It has to know the exact address to call, the exact way to phrase the request, and the exact format of the answer. When it guesses wrong, which it often does, it invents an action that does not exist or sends a malformed request, and you get an error or, worse, the wrong change made to a live campaign.

MCP removes the guessing. The available actions are handed to the assistant up front, each one labeled and described. The AI is no longer reverse-engineering an API. It is choosing from a clear menu, the same way you would pick from a list of options. That is why MCP feels reliable in a chat window when raw API access feels brittle.

The practical upshot for you: you do not phrase a request to an API. You phrase it to a person.

How are my campaigns doing this week across Meta, Google, and TikTok?

The assistant finds the reporting action, runs it, and writes you a normalized summary. There is no URL, no header, no syntax. If you want to go deeper on this comparison, see MCP vs REST for AI agents.

Which AI apps support MCP today

Adoption moved fast. As of mid 2026, you can use MCP servers like Xylo in all of these:

Claude, on claude.ai or in Claude Desktop, by adding a connector.
ChatGPT, on any paid plan, on the web, in Developer mode.
Cursor, the AI code editor.
Claude Code, Anthropic's command-line agent.
Codex, OpenAI's coding agent.
Anything else that is MCP-compatible, since the protocol is open.

You only need one of these. Most marketers and brand owners use Claude or ChatGPT and never touch the rest.

What this looks like for managing ads

Traditional ad management gives you two choices: click around the platform UI yourself, or pay someone to write code against the APIs. MCP adds a third, and it is the one most people actually want: just ask.

Connect Xylo to your assistant once, and your everyday account work turns into plain requests:

Show me every Meta and TikTok ad set with a cost per purchase above $30 this week.

Pause the three worst performers and move their budget to the top campaign.

Build a lookalike audience on Meta from my last 90 days of purchasers.

Why did my Google Ads cost per conversion jump on Tuesday?

Send me a Friday report across all three platforms.

Each of these is minutes of clicking, exporting, and cross-referencing if you do it by hand. Through an assistant with Xylo connected, they take a sentence.

A few things make this safe to do on real accounts. New campaigns are created paused, so nothing spends until you look at it and approve. Your account access is encrypted (AES-256), and the assistant never sees your actual login or tokens. It can only use the specific actions Xylo exposes, nothing more.

Connect Xylo to your AI agent

Getting started takes about a minute and no code.

Sign in. Create a free Xylo account, then authorize Meta, Google, or TikTok with a normal OAuth login and pick the account you want to manage. Your tokens are encrypted and stay with Xylo, not your assistant.
Add Xylo to your AI app. In Claude or ChatGPT, add a connector pointing at the Xylo MCP endpoint:

https://xylomcp.com/api/mcp
Start talking. Ask your assistant about your campaigns. It will use Xylo's tools to pull real numbers and make the changes you approve.

The full walkthrough lives in the docs, and the prompt library has ready-to-paste requests for reporting, optimization, audience building, and creative analysis.

For developers (optional)

If you would rather wire things up yourself, Xylo also has a plain REST API at https://api.xylomcp.com. You authenticate with an x-api-key header, point Meta calls at an ad account with x-ad-account, and get back a consistent { data, meta } response shape. Budgets are in dollars, and new campaigns default to paused, same as the MCP path. It is the same engine underneath, just addressed directly. Most people never need it, because the MCP connector already does the job in plain English. For the build-it-yourself route, see building AI agents for ads.

Where this is heading

MCP is still young, but the direction is set. As more services publish MCP servers and more AI apps support them, the gap between "I want this done" and "it is done" keeps shrinking. For advertising, that shift is already here: you stop building integrations and start describing what you want.

Try it on your own accounts. Connect Xylo to Claude or ChatGPT, start on the free tier, no card required, and ask your assistant to run your next report.

Connect Xylo free