An open standard that defines how AI models connect to external tools, data sources, and services — the universal adapter that allows agentic AI workflows to plug into your existing stack without custom engineering for every new combination.
Also known as MCP, agent protocol
MCP is to AI agents what USB-C is to hardware: a standardized connection that makes interoperability possible without rebuilding integrations from scratch every time. Before MCP, connecting an AI tool to a data source meant custom engineering for each pairing. With MCP, any compliant tool can plug into any compliant data source through a shared protocol.
Introduced by Anthropic and rapidly adopted across the AI ecosystem, MCP defines how an AI model requests information from a tool, how the tool responds, and how the exchange is structured so either side can be swapped out independently. The result is a modular AI stack rather than a fragile web of one-off integrations.
Agencies are increasingly in the business of recommending, procuring, and building AI tools — for themselves and for clients. MCP is now a meaningful differentiator in that process. “Is this MCP-compatible?” is the new “Does this integrate with our stack?” and the answer has real implications for how flexible and future-proof a tool actually is.
Vendor evaluation. An MCP-compatible platform can connect to other MCP-compatible tools without custom work. Switching one component doesn’t require rebuilding every integration. Agencies that understand this can ask sharper procurement questions and avoid lock-in.
Building your own automation. If your agency is building agentic workflows, MCP determines how easily those workflows connect to the data they need. A MCP-based setup can link an AI agent to a CRM, a project management tool, a brand asset library, and a reporting dashboard through a single standard rather than four separate integrations.
An agency wants to build an AI assistant that pulls a client’s campaign data from their ad platform, cross-references it against brand guidelines in a shared drive, and drafts a performance summary in the client’s reporting template. Without MCP, each of those connections requires custom engineering. With MCP-compatible tools, the workflow plugs into each data source through one protocol — when the client switches ad platforms, only that one connector needs to change. The rest of the system stays intact.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.