Could you explain MCP to my friend?

By Trailblaze Labs | Published 2025-04-11 | Education | 6 min read

Model Context Protocol is the standard connector that lets AI tools work with outside data and systems. Think of it as USB-C for AI.

Yes, it is definitely my friend who needs the plain-language version.

When I first wrote about Model Context Protocol, it was easy to treat it as another technical acronym. Since then, MCP has become a standard connector for AI products. It does not create shared memory by itself. It gives AI clients a consistent way to discover and use outside tools, data, and workflows.

Wait, what's MCP?

MCP stands for Model Context Protocol. It is an open standard for how an AI client can connect to a server that offers tools or data.

Translation: MCP gives AI tools a shared language for asking what is available and using it in a consistent way.

Imagine an AI assistant that can look up a customer in your CRM, read an approved document, or create a task in another system. Before MCP, each connection often required its own custom integration. MCP provides a common pattern for those connections.

The USB-C analogy is useful. One standard connection can support many different tools, as long as both sides follow the protocol.

Why did it take off?

MCP addressed a practical integration problem at the right time. AI products needed access to more than a chat window, and developers did not want to rebuild the same connector for every model and application.

Translation: It is specific enough to be useful, open enough for different companies to adopt, and simple enough for developers to build around.

MCP can help an AI retrieve relevant context when it needs it. The memory, permissions, and source data still have to come from the connected systems.

The fine print

A common connector also creates common questions that businesses need to answer:

  1. Authentication: Who is allowed to connect and act? A weak identity check can expose data or tools to the wrong user.
  2. Trust: How do you know an MCP server is secure and legitimate? Protocol support is not a security review.
  3. Provisioning: Who approves, configures, and removes each connection? Setup is easier than it was, but organizations still need ownership and controls.
  4. Security: Every new connection expands what an AI may be able to read or do. Use least-privilege access, review the tools being exposed, and keep sensitive actions behind approval.

So where are we today?

MCP is no longer limited to small developer experiments. Many AI products and software platforms now support it, and the basic connector pattern is becoming familiar. The technical work has improved, but business owners still need to decide which systems may connect, what each connection may do, and how activity will be reviewed.

That can support workflows such as:

  • ChatGPT retrieving approved brand guidance before drafting copy
  • Notion updating a roadmap from a meeting note
  • A CRM providing account context before an outreach draft
  • Gamma applying approved brand standards across saved presentation themes
  • A research assistant using authorized travel and loyalty data instead of asking for the same details again

Those examples do not require every application to share one memory file. They require each approved system to expose the right context or action through a consistent connector.

Bottom line

MCP has become common infrastructure for connecting AI to useful systems.

Its value depends on the quality of the connected data, the usefulness of the available tools, and the controls around access. Start with a clear workflow, expose only what that workflow needs, and review the connection like any other production integration.