McP: A Simple Guide to Going Further With AI

The Model Context Protocol might sound like technical jargon, but it represents a significant shift in how AI applications will connect with real-world tools and agents.

In simple terms, MCP is a protocol that allows AI applications (the "MCP Clients") to communicate with various services (the "MCP Servers"). Think of these servers as offering specialized capabilities—or "tools"—that the AI can leverage when needed.

What makes this revolutionary is the standardization. With MCP, any AI that speaks the protocol can instantly tap into an (exponentially growing) ecosystem of servers. This dramatically amplifies what systems like Claude, ChatGPT, and agent.ai can accomplish without custom coding for each integration.

LLMs have historically been limited to the data they were trained on. This is what's often known as the "knowledge cutoff date."

To work around that limitation, two major innovations emerged:

1. RAG (Retrieval-Augmented Generation): whereby we put relevant information right into the context window. (We retrieve that relevant information by using what’s called a vector database—but that’s a topic for another day).

2. Tool Use: whereby the LLM is given access to a set of "tools" it can use to respond to a prompt

The problem with tool use has been that for a tool to be available in the LLM, it had to be specifically coded into the AI application (like ChatGPT or Claude). Adding new tools required updating the AI application itself.

MCP simplifies and decouples things. Think of it like the USB-C standard.

If you build a new laptop, all you need to do is support the USB-C standard and add USB-C ports. Then all existing USB-C devices will work with your laptop. Similarly, if you make a new peripheral device that supports USB-C, it will work with all computers that support the standard.

That's exactly what MCP does for AI applications:

• AI applications (Clients) can connect to any MCP Server

• Applications and tools (Servers) only need to implement the MCP standard once

Before MCP, having your CRM data accessible to three different AI systems required three separate integrations. Each developer would need to know how to use the API (Application Programming Interface) of the CRM and then write specific code for each possible use case, calling the API as needed.

Now, implement the standard model context protocol once, and you're compatible with every AI app, agent or system that speaks MCP.

The real magic happens when you combine tools from multiple MCP servers to accomplish higher-level goals. I predict by the end of the year, there will be thousands of MCP Servers that will make it possible to power AI apps and agents with all sorts of data and capabilities.

it's a simple idea: let AI talk to your favorite tools using a standard language they all understand.

"Find all the deals in my pipeline that haven't moved in 30 days, analyze the last conversation notes for each, and suggest personalized next steps based on the customer's history."

Imagine typing that into your AI assistant and getting a complete analysis and action plan in seconds. That's what's now possible with HubSpot's new MCP integration.