The Model Context Protocol (MCP) is an open standard that defines how AI assistants connect to external tools, data sources, and services. If you have asked yourself how Claude connects to your ad accounts, the short answer is MCP: a standardized communication layer that lets an AI model call specific functions on a remote server, receive structured data back, and act on it within a conversation.
This article explains how MCP works technically, what the tool-calling envelope actually looks like, and how implementations like Adsroid MCP translate that protocol into real advertising actions inside Google Ads and Meta Ads.
What Is the Model Context Protocol?
MCP was introduced by Anthropic in late 2024 as a way to standardize how AI models interact with the outside world. Before MCP, every integration between an AI assistant and an external service was custom-built. Developers had to write bespoke connectors, handle authentication differently for every platform, and rebuild context-passing logic from scratch each time.
MCP solves that by creating a shared protocol. Think of it as a USB-C standard for AI integrations: instead of every device needing its own cable, one common interface works everywhere that adopts it.
MCP does not make AI smarter. It gives AI a reliable, standardized way to reach outside the conversation and interact with real systems.
The protocol defines three core concepts:
- Tools: callable functions that the AI can invoke, each with a defined name, description, and parameter schema.
- Resources: static or dynamic data the AI can read, such as files or database records.
- Prompts: reusable templates that shape how the AI approaches a task.
For advertising use cases, tools are the most relevant primitive. An MCP server built for ad management exposes a list of tools, and the AI calls those tools to read campaign data, modify budgets, or create new ads.
How Does MCP Tool Calling Work?
When an AI assistant like Claude is connected to an MCP server, it receives a manifest of available tools at the start of a session. Each tool entry includes a name (for example, get_campaign_performance), a plain-language description, and a JSON Schema defining the expected parameters.
During a conversation, when the user asks something like
