MCP (Model Context Protocol) for Dummies 🫣

Understanding Model Context Protocol (MCP): A Clear Explanation

Over the past week, the topic of MCP (Model Context Protocol) blew up on X. It was interesting to note that many of the posts - while there were some good explanations - some were either vague or too technical, and some were erroneous or contained conflated concepts.

I came across a LinkedIn post by a person with quite a lot of followers and they compared MCP to being the Zapier killer, which makes it pretty obvious that there's a misconception around MCP and MCP seems complex or overwhelming to those who've not used it and do not understand it. MCP is not directly comparable to Zapier, as they serve different purposes (MCP is a protocol, not an automation platform).

In this blog post, I hope to clarify this and dispel these misconceptions by providing just a clear example in a basic way as to what exactly MCP is and what it's not. First off, it's important to note that MCP is not in itself an API in the traditional sense of a mechanism whereby you can offer or expose endpoints to a consumer (e.g. Google has an API for developers to read from Google Documents or other Google Services once authorized).

What is MCP?

• MCP is like a universal translator between AI assistants and your everyday apps.
• Without MCP: AI is limited to what it learned in training (like using an old encyclopedia).
• With MCP: AI can access live information and perform actions in your apps.

How MCP Works (Key Components)

• User: Asks questions or makes requests in plain language.
• MCP Client: The chat interface where you type your request.
• AI Model: Understands what you're asking for.
• MCP Servers: Special connectors for each service (Gmail, Drive, etc.).
• Your Apps: Where your actual data lives (emails, files, calendar).

Real-World Example

• You ask: "Summarize the emails about Project X and schedule a team meeting."
• AI understands your request and determines it needs access to emails and calendar.
• MCP Servers fetch the relevant emails and check team availability.
• AI creates a summary and schedules the meeting with proper participants.

Everyday Benefits

• Search through files and emails using conversational language.
• Get personalized recommendations based on your actual documents.
• Schedule meetings with automatic time zone adjustments.
• Draft emails with context from multiple sources.
• Organize information across different apps with a single request.

Why MCP Matters

• Transforms AI from "information only" to "gets things done."
• Keeps your data secure (MCP only accesses what you permit).
• Creates a standard way for any app to connect with AI.
• Allows developers to build new connections for more services.

In closing

MCP is basically a bridge between a large language model that you speak with (or chat with) and it provides additional capabilities to that large language model, enabling access to external data and information that it previously did not have access to.

You could have an MCP server connected to Claude Desktop app, allowing it to have search functionality. You could also use MCP to enhance the model's interactions through access to external tools and data sources. Additionally, the MCP allows for read and write of files on your computer, so you could have it create projects, create files, or read files to provide additional context to the chat you're having with Claude.

MCP is part of a broader trend toward making AI assistants more capable through tool use and external connectivity, moving beyond the limitations of their training data.

Hopefully this clears it up and makes sense to you, and helps you understand what MCP is.

If there's enough interest, I will look at follow-ups to this. The next post could cover things like tool use and external resources and even just getting started with Claude Desktop or one of the code editors or code assistants (Cursor, Windsurf, or Cline) and explaining the difference between an MCP server and an MCP client.