If you’ve ever talked to ChatGPT or used an AI tool that remembers your preferences, you’ve experienced a small part of how AI models use context to work better for you. But what happens when multiple AI tools or “agents” work together? How do they stay on the same page?
The answer lies in a concept called Model Context Protocol, or MCP.
What is MCP (Model Context Protocol) in AI?
MCP is like a shared notebook that AI agents use to write down and read important information.
This includes:
- What the user asked
- What has been done so far
- What each agent (tool) is responsible for
- Any results or decisions made along the way
With MCP, each AI agent can focus on its job while understanding the big picture just like how teammates collaborate using a shared Google Doc in a project.
A Simple Analogy
Let’s imagine you’re planning a trip. You talk to three friends:
- One finds flights
- One suggests hotels
- One helps you rent a car
Now, if each friend works without knowing what the others are doing, they might repeat work or give you suggestions that don’t fit your plans.
But if they share a notebook, where you wrote:
“I’m going to Tokyo from May 5–12, prefer morning flights and 4-star hotels.”
Now each friend can read that info and tailor their suggestions. That notebook? That’s what MCP is for AI agents.
Our Hands-On AI Project
We’ll build a simple project that demonstrates this idea. Here’s what it does:
- A user says they want to travel to Tokyo.
- The Flight Agent suggests flight options.
- The Hotel Agent recommends places to stay.
- All agents share and update a central memory (the MCP) that holds everything.
MCP ensures they all know your budget, destination, and travel dates without asking over and over.
Visual Breakdown of MCP in Action
In the above setup:
- The user gives an instruction
- Central MCP holds the context
- Each Agent reads from and writes back to the MCP
The context evolves with each step, and each model/tool only needs to focus on its task.
Next Step:
In the future, we’ll build our example:
- Adding more agents like car rentals or restaurant finders
- Turning the script into a chatbot or web app
- Visualizing how context changes over time
- Deploy MCP Travel Planner to AWS using a simple and cost-effective method.
If you’re curious about AI, and automation, or just love building cool stuff step-by-step, stay tuned and subscribe to our blog!
