What is MCP and Why Should I Care?
In the last article, we talked about agents: AI that can take actions, not just answer questions. The natural follow-up question is: how does an agent actually connect to your tools? How does it read your calendar, check your files, or interact with your software?
That is what MCP solves. If agents are the "what," MCP is the "how."
The problem MCP solves
MCP stands for Model Context Protocol. It is a standard that lets AI tools connect to external software: your files, your databases, your calendar, your email, your code editor, your project management tool. Instead of you being the middleman who copies information back and forth, MCP lets the AI reach out and get what it needs directly.
Here is a concrete example. Without MCP, if you want AI help planning your week, you open your calendar, read through your meetings, type them into the AI conversation, and then ask it to help you find free time. With MCP, you say "look at my calendar and find open blocks this week" and the AI connects to your calendar, reads it, and gives you an answer. Same question, dramatically less friction.
How it works (without the jargon)
MCP is a communication standard. It defines how an AI tool talks to other software. Think of it as a shared language: any AI that speaks MCP can connect to any tool that speaks MCP, without needing custom integration for every combination.
There are three pieces:
The AI tool (like Claude or ChatGPT) is the client. It is the one asking for information or requesting an action.
The MCP server is the bridge. It sits between the AI and whatever software you want to connect. There is a server for Google Calendar, a server for your file system, a server for Slack, a server for your database. Each one translates between MCP and the specific tool's language.
The external tool is the destination. Your calendar, your files, your email. It does not need to know anything about AI. The MCP server handles the translation.
This means you do not need to wait for each AI company to build a direct integration with every tool you use. Anyone can build an MCP server, and any AI tool that supports the protocol can use it.
Why it matters
MCP was created by Anthropic (the company behind Claude) and donated to the Linux Foundation in late 2025, making it an open standard that no single company controls. The ecosystem is not locked to one provider.
The practical impact is that AI is moving from a tool you talk to into a tool that can act within your existing workflow. In the last article, we talked about agents. MCP is the infrastructure that makes agents useful. An agent without MCP can only do what it can figure out from the conversation. An agent with MCP can read your files, check your data, and interact with your tools.
This is also directly connected to the context window problem from Day 6. Instead of pasting an entire document into the conversation and hoping the AI finds the relevant part, MCP lets the AI query a database or search your files for exactly what it needs. Less wasted context, more focused results.
What this looks like in practice
If you use Claude, you may have already used MCP. Claude's ability to connect to Google Drive, search the web, or interact with code editors all use MCP under the hood.
For developers, the ecosystem is growing quickly. There are MCP servers for GitHub, Slack, databases, file systems, and dozens of other tools. Setting one up requires some technical configuration, but the pattern is straightforward: install the server, point it at your tool, tell your AI client where to find it.
For non-developers, the experience is simpler. The AI tools handle the MCP connections behind the scenes. You just get better, more connected responses without having to understand the plumbing.
Do you need to care about this right now?
For everyday AI use, probably not yet. The tools are handling MCP connections for you, and the most common integrations (web search, file access, calendar) are increasingly built into the products you already use.
The reason to know about it is that MCP explains why AI tools are getting more capable so quickly. It is not just that the models are getting smarter. It is that the models are getting connected. A smarter model that can only see what you paste into it is limited. A model that can reach into your actual workflow is a fundamentally different tool.
If you are a developer or a technical user who builds workflows, MCP is worth learning now. The protocol is well-documented, the ecosystem is active, and early adopters are building integrations that save significant time.
For everyone else, watch for it. When your AI tool starts offering to check your calendar, read your documents, or connect to your work tools, that is MCP. When it works, it is the moment AI stops feeling like a separate application and starts feeling like something woven into how you already work.
Next time: the last piece of the puzzle. Everyone keeps saying "RAG" and you keep nodding along. Time to fix that.
If there is anything I left out or could have explained better, tell me in the comments.
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