Imagine a plumbing system that lets large language models connect to other software in a predictable and controlled way. This is what Model Context Protocol (MCP) does – it’s a specification developed by Anthropic that outlines how models interact with external tools and data, enabling AI to perform tasks across different systems.
In practical terms, MCP defines which actions a model is allowed to use, how to call each one, and where the limits are. This means developers don’t need to build custom integrations for every use case. Instead, they define what their software can do using the MCP standard format, and AI systems can string those actions together to complete workflows.
MCP is being adopted in various industries, including ad tech. It’s used behind the scenes by companies like Criteo and Adverity to query campaigns, assemble audiences, and set up campaigns in natural language. The protocol also helps media teams analyze competitors and market trends without having to switch between tools.
However, there are still open questions surrounding MCP. Some marketers worry that using AI will lead to forgetting what’s already been asked or losing context. Others point out the need for unified models, automated checks, and data governance to make the most of this protocol.
Despite its challenges, MCP has the potential to simplify the complex workflows of ad tech by providing a universal adapter that makes AI more usable for marketers. As one expert puts it, “MCP is like a USB – it’s the connector between AI applications and external tools.”
Source: https://www.adexchanger.com/adexplainer/understanding-mcp-the-universal-adapter-for-ai-in-advertising