AI is getting smarter every day—but for the longest time, it had one frustrating flaw: it couldn’t remember past conversations. Whether you were chatting with a virtual assistant or using a customer support bot, the experience often felt impersonal. You’d have to re-explain who you are, what you wanted, and what you talked about last time. Annoying, right?
That’s exactly what the Model Context Protocol (MCP) is here to fix. In this article, you'll discover what is model context protocol, how it's reshaping AI interactions, fostering deeper connections, enhancing user experiences.
What Is Model Context Protocol?
Developed by Anthropic, MCP allows AI to remember useful details from previous interactions—like your preferences, recent questions, or ongoing tasks. With that context, conversations feel more natural, helpful, and human.
It’s a major step forward for everything from chatbots to virtual assistants. MCP doesn’t just improve convenience—it transforms AI into something that acts more like a real partner than a search box.
At its core, a Model Context Protocol (MCP) server helps AI remember things. It stores helpful details (like your preferences or past conversations) and shares them with AI models when needed. Instead of treating every chat like the first one, the AI uses the MCP server to pick up where you left off.
So now, if you ask an AI to schedule a meeting today and come back tomorrow asking for a reminder, it remembers. That’s thanks to the MCP server quietly working in the background.
Why MCP Matters
So, why all the hype around MCP? It changes the game.
With traditional AI, every session feels disconnected. But with the Model Context Protocol, you get smarter, more consistent experiences. Ask something today, follow up tomorrow—your AI remembers.
That’s huge for customer service bots, personal assistants, and even tools that help with writing, coding, or research. MCP makes AI more human, more personal, and a lot less robotic.
Main Components of the Model Context Protocol
All Alright, let’s break it down. What are the main components of the Model Context Protocol?
1. Memory Layer
This is where past interactions, preferences, and key details are stored. It’s the backbone of MCP’s memory.
2. Context Engine
The brain of the operation. It filters through memory and pulls up what’s relevant based on what you’re currently doing.
3. MCP Server
This handles requests and coordinates between apps, sessions, and tools. Think of it as the central hub keeping everything connected.
Together, these parts let the AI maintain a smooth flow of context—so you’re not constantly repeating yourself.
Key Features of Model Context Protocol
MCP servers are where the magic happens. They power the prompts, connect to resources, and activate tools—all the things that make AI not just responsive but proactive.
Prompts
Think of prompts like cheat codes. They guide how the AI responds and let users trigger specific tasks with slash commands or simple clicks.
- Purpose: Keeps conversations focused.
- User control: You pick prompts; AI follows.
- Customization: Easily adapt prompts to different needs.
Resources
This is your AI’s knowledge base. Documents, files, data sets—anything the system needs to understand your world.
- Purpose: Gives the AI context.
- Flexibility: Show resources in lists, search tools, or auto-display.
- Real-time updates: AI stays up to date as resources change.
Tools
The real action items. Tools let AI do more than talk—they let it do.
- Examples: Book a meeting, send an email, pull a report.
- Purpose: Take action on your behalf.
Together, prompts, resources, and tools give MCP its real-world muscle.
Benefits of MCP
Smarter Conversations
MCP makes AI feel more human. It remembers your last chat, your tone, and what you care about. That means more relevant replies and fewer awkward reintroductions.
Personalization That Feels... Personal
Told your AI you’re more productive in the mornings? Cool—it remembers. It’ll schedule meetings at the right time without needing reminders.
Seamless App Integration
MCP talks to your calendar, emails, and project management tools, keeping everything in sync. No more app-hopping.
Better Teamwork Between AI Agents
Have you got multiple AI agents doing different things? MCP helps them share info and collaborate without stepping on each other’s toes.
Effortless Multitasking
Ask about the weather in the middle of scheduling a meeting. No problem. MCP keeps both tasks in check and picks up right where you left off.
Chaining MCP Servers
One of MCP’s most exciting superpowers lies in its ability to chain multiple servers together seamlessly, enabling complex workflows that mimic true AI collaboration. Unlike traditional APIs that require brittle logic and careful handoffs, MCP lets hosts fluidly coordinate across many specialized servers.
Let’s say you want your AI to organize an offsite team. With chained MCP servers, it could:
- Pull calendar data from your team’s Google Calendars (Calendar MCP server),
- Use the Weather MCP server to find ideal dates with clear skies,
- Query travel options using a Flights or Maps server,
- Book a venue via OpenTable or Eventbrite,
- Then, summarize the plan and post it in a Slack channel.
The host manages the whole process using plain language—no coding or complicated setups needed. It’s a big step toward AI that can handle complex tasks with minimal help.
MCP Integration: A Real Example
Without an MCP, every interaction with the AI assistant is independent, and the system forgets all previous interactions once the session ends. Each time you return, the assistant asks for the same information, and context is lost.
When MCP is integrated, the AI model gets the context from the previous discussions and if you ask a question about your schedule one day, then return the next day and ask for a reminder, the AI will remind you about the upcoming meetings without needing to ask for your availability, as it has full information about it.
Below, you can see a simple example of integrating MCP into a Python-based AI assistant:
from mcp_server import MCPServer
from mcp_client import MCPClient
# Initialize MCP server
server = MCPServer()
# Create MCP client and bind it to the server
client = MCPClient(server)
# Store context information
client.set_context("user_name", "John Doe")
client.set_context("preferred_time", "2:00 PM")
# Retrieve context information for a personalized response
user_name = client.get_context("user_name")
preferred_time = client.get_context("preferred_time")
# AI response based on stored context
print(f"Hello {user_name}, your preferred meeting time is {preferred_time}.
Would you like to schedule a meeting?")
Now, the AI sounds less like a machine and more like an assistant who knows you.
MCP Servers You Should Try
If you're ready to try out the Model Context Protocol in your projects, you’ve got a few great server options to pick from. These tools make it easier to give your AI apps memory, context, and smarter responses—without starting from scratch.
Anthropic’s MCP Server
This one’s from the team that created MCP in the first place. Anthropic’s server is reliable, fast, and made to work smoothly with their AI assistant, Claude. It's a great choice if you want something that just works right away, with solid performance and support behind it.
Think of it as the "official" version—simple to set up and powerful out of the box.
OpenMCP
OpenMCP is a free, open-source option built by the community. It gives developers more freedom to tweak and customize things the way they want. If you're comfortable with some coding and want full control, this one’s a solid pick. It’s flexible, lightweight, and keeps getting better, thanks to community contributions.
It’s a hands-on choice, but great if you enjoy building things your way.
Custom MCP Plugins
Lots of platforms now offer built-in MCP features or plug-ins you can just plug and play. These are perfect for people who don’t want to deal with server setup or heavy development. Just install the plugin, connect it to your tools, and you’re good to go.
Ideal for small teams, startups, or anyone who wants to get started fast without the hassle.
Chaining MCP Servers
One of MCP’s most exciting superpowers lies in its ability to chain multiple servers together seamlessly, enabling complex workflows that mimic true AI collaboration. Unlike traditional APIs that require brittle logic and careful handoffs, MCP lets hosts fluidly coordinate across many specialized servers.
Let’s say you want your AI to organize an offsite team. With chained MCP servers, it could:
- Pull calendar data from your team’s Google Calendars (Calendar MCP server),
- Use the Weather MCP server to find ideal dates with clear skies,
- Query travel options using a Flights or Maps server,
- Book a venue via OpenTable or Eventbrite,
- Then, summarize the plan and post it in a Slack channel.
The host manages the whole process using plain language—no coding or complicated setups needed. It’s a big step toward AI that can handle complex tasks with minimal help.
How to Set Up MCP Servers
Getting started with MCP is easier than you think:
- Choose your MCP server (e.g., Anthropic or OpenMCP).
- Connect your AI assistant or tool.
- Configure prompts, tools, and resources as needed.
- Test interactions to make sure context flows smoothly.
- Refine as you go. Fine-tune memory settings and tool usage for better results.
Setting Up MCP Servers with Solicy
Solicy’s backend developers specialize in building robust, scalable systems using technologies like Node.js, Python, and.NET.
Whether you're launching a new platform or scaling an existing one, the team can help you set up and optimize your MCP server to ensure smooth, reliable performance.
With proven experience handling over 1 million users simultaneously, Solicy delivers backend solutions that are efficient, secure, and built to grow with your business.
Conclusion
The Model Context Protocol isn’t just a cool feature—it’s the backbone of smarter, more helpful AI. With components like prompts, resources, and tools—and powered by solid MCP servers—it gives both users and developers a huge advantage.
For users? It means no more repeating yourself. For devs? It’s a cleaner, more powerful way to build AI systems that remember, adapt, and connect.
So, whether you’re building the next great assistant or just want your AI to finally get you, MCP is the future. And it’s here to stay.
Model Context Protocol: FAQs
1. What is Model Context Protocol in simple terms?
MCP is a way for AI to remember important details from your past interactions—like your preferences, past questions, or ongoing tasks. It helps AI feel more natural and personal by keeping track of context across conversations.
2. Is MCP only for big companies or advanced AI systems?
Not at all. MCP can be useful for anyone building AI tools—startups, small dev teams, and even solo creators. It helps make your AI smarter, more personal, and easier to use, no matter the scale.
3. Does using MCP require coding skills?
Not necessarily. While developers can build custom integrations, many platforms now offer plug-and-play MCP plugins that require little to no coding.
4. What is the difference between MCP and RAG?
MCP (Model Context Protocol) and RAG (Retrieval-Augmented Generation) both aim to improve how AI accesses and uses external information—but they work differently.
- MCP is a protocol that lets AIs connect to tools, resources, and memory systems dynamically—kind of like plugging apps into your AI.
- RAG enhances responses by letting the AI search and retrieve relevant documents during a query.
5. What is Model Context Protocol in Minecraft?
If you’re seeing MCP in the context of Minecraft, it refers to something completely different: the Minecraft Coder Pack. That version of MCP is a set of tools used by modders to decompile and modify Minecraft's code—not related to AI or Anthropic’s Model Context Protocol. Totally separate worlds—just the same abbreviation.
