An open protocol that standardizes how AI models connect to external data sources and tools, providing a universal interface for giving AI systems access to the context they need.
MCP standardizes how AI models connect to external tools and data. Before MCP, connecting an AI model to your CRM, database, file system, or any other tool required custom integration code for each connection. MCP replaces that with a universal protocol: build one MCP server for your data source, and any MCP-compatible AI client can use it.
The analogy is USB. Before USB, every peripheral needed its own cable and port. USB standardized the connection. MCP does the same for AI-to-tool connections.
How it works
An MCP server wraps an existing tool or data source, exposing its capabilities in a standardized format. An MCP client (an AI application) discovers what the server offers and interacts with it through the protocol. The model can read data, call functions, and receive structured responses without knowing anything about the underlying system’s implementation.
Why it matters for martech
Martech stacks have dozens of tools. Connecting AI capabilities to each one individually is the same integration spaghetti problem the industry has faced for years. MCP provides a standardized connection layer that could reduce the integration effort from per-tool to per-protocol, making it practical to give AI systems access to your full marketing technology ecosystem.
What most people get wrong
MCP is infrastructure, not intelligence. It gives AI models access to tools and data. It does not make the model smarter or more capable. The quality of the output still depends on the model, the prompt, and the context engineering around it. MCP solves the connectivity problem. What happens after the connection is established is a separate challenge.