What Is Model Context Protocol? Future of Seamless AI Experiences

Artificial Intelligence has come a long way. From answering basic questions to writing poems and solving complex problems, AI has started to feel like an intelligent partner rather than a tool. But have you ever wondered how AI remembers what you're talking about? Or how it seems to know the flow of your conversation, even if you don’t repeat yourself every time?

Model Context Protocol (MCP) plays a crucial role in enhancing the intelligence, smoothness, and usefulness of AI tools across various apps and platforms.

So, What Is Model Context Protocol all about? Let’s find out.

What Is the Model Context Protocol (MCP)?

MCP is a two-way communication bridge between AI assistants and external tools, allowing the AI to obtain information and, more importantly, take action.

It's an open-source protocol for securely connecting AI tools to data sources such as your company's CRM, Slack workspace, or development server. That means your AI assistant can draw in relevant data and take actions in those tools, such as changing a record, sending a message, or initiating a deployment. MCP allows AI assistants to understand and act, resulting in more useful, context-aware, and proactive AI experiences.

MCP was developed by Anthropic (the firm behind Claude), but it has now been adopted by OpenAI as well as AI platforms such as Greta, Zapier, Replit, Sourcegraph, and Windsurf.

Key Features of the Model Context Protocol:

The MCP simplifies interactions between AI models and external systems, providing a solid platform for scalable and intelligent integrations. Here are some of its main features:

1. Dynamic tool discovery - Models can automatically detect and interact with new tools or services, eliminating the need for human configuration.
2. Context-aware state management - MCP preserves context across API calls, allowing models to execute complicated workflows more precisely.
3. Built-in security and access control - Authentication and access control technologies are integrated to ensure secure, authorized access to critical data.
4. Lightweight JSON-RPC communication - MCP's lightweight JSON-RPC communication ensures efficient and low-latency communication between models and external services.
5. Interoperability and extensibility - It integrates with other tools and may be expanded to accommodate new technologies.
6. Developer-Friendly Design - MCP's simple structure and standardized methodology simplify integration efforts and speed up development.
7. Scalable and flexible architecture - MCP's architecture is scalable and versatile, allowing it to support systems of any size and adapt to evolving needs.

Why Was MCP Created?

Here’s the problem: Until now, AI models have largely worked in isolation. If you’re using an AI in a calendar app, it doesn’t know anything about your project notes from your to-do app. If you switch devices, the model often “forgets” your preferences or past interactions.

That’s a pretty frustrating user experience.

MCP solves that by providing a standardized way to share relevant, structured context with models always securely, always with user consent.

Why Do We Need a Context Protocol Anyway?

Imagine you’re chatting with an AI assistant about planning a vacation. You say:

• “I want to go somewhere warm in July.”
• Then later: “Book a hotel with a pool.”
• Followed by: “What’s the average temperature there?”

If the AI doesn’t remember the earlier parts of the conversation, “there” becomes meaningless.

That’s where the Model Context Protocol comes in. It keeps track of the flow of conversation so the AI can respond meaningfully without you repeating yourself.

Without MCP, the interaction would go something like:

You: “What’s the average temperature there?” AI: “Where exactly are you referring to?” You: “I mentioned I want to go somewhere warm in July…” AI: “I’m sorry, I don’t remember that.”

And that’s not helpful.

What Kind of Context Does MCP Handle?

MCP can carry and organize various types of information, including:

• Your preferences (e.g., "always prefers metric units")
• Your past interactions with the model
• Relevant data from other connected apps or tools
• Your workspace settings or team environment
• Your goals or current projects

This context helps the model understand not just what you’re asking, but why — and that’s where the magic happens.

How Does MCP Work?

MCP is built on a client-server model that includes the following main parts:

• Hosts: These are the programs that users communicate with, like Claude Desktop, an IDE like Cursor, or an agent that was built just for them.
• Client: A piece of software that is built into the Host app and connects straight to one MCP Server. The Host makes three Clients if it needs to talk to three outside services.
• Servers: Servers are outside programs that let the AI model access Tools, Resources, and Prompts through the client. They connect what the model can do to what the system can do.

This is a simple explanation of how a Client and Server usually talk to each other in the MCP architecture:

1. Initialization: The client and server share protocol information to ensure they are on the same version and can communicate.

2. Discovery: The client asks the server for a list of all the Tools, Resources, and Prompts.

3. Context Provision: The user is shown relevant resources and hints, or they are built into the Host environment so the AI can use them.

4. Invocation: The AI uses its thinking to figure out when a tool needs to be used and tells the client to send the right request to the right server.

5. Execution: The server handles the request and does things like talking to external APIs or getting to local data.

6. Completion: The results are sent back to the AI, which uses them to move the conversation forward or give the person a final answer.

Model Context Protocol (MCP) and AI Agents

Is MCP Safe? What About Privacy?

Absolutely, privacy is core to the MCP design.

Here’s how it keeps users in control:

• Explicit user consent: Nothing is shared unless you approve it
• Granular permissions: Choose what data types or apps to allow
• No centralized data hoarding: MCP isn’t about collecting everything — it’s about connecting context
• Security-focused: MCP is built with end-to-end encryption and safe communication between apps and models

You always decide what the AI knows and when. That’s non-negotiable.

What Does This Mean for Developers?

If you’re building apps that integrate with AI, MCP is your gateway to:

• Smarter, more personalized model outputs
• A consistent user experience across apps
• Easy onboarding for models that already understand context
• The ability to reduce repetitive input from users

OpenAI and other AI model providers can use MCP to plug directly into your app’s context, with user permission, and deliver more intelligent responses from day one.

Final Thoughts: Why It Matters

AI without context is like a smart person with amnesia. Impressive, sure — but disconnected.

With Model Context Protocol, AI becomes more helpful, more intuitive, and more human-like in its interactions. You get the benefits of personalization without sacrificing privacy. And developers get a better way to build connected, intelligent experiences.

The future of AI isn’t just about bigger models. It’s about smarter context, and MCP is a big step in that direction. Book a call with us today to understand more about what Greta does.

FAQs

1. What is Model Context Protocol (MCP)?

MCP is an open standard that allows developers to connect AI models to external tools, apps, and data sources, enabling smarter and more context-aware AI interactions.

2. Does using MCP make an AI tool an autonomous agent?

Not necessarily. MCP enables integration, but autonomy comes from how the AI is built to use that context for decision-making.

3. Who should use MCP?

MCP is best suited for developers or technical teams building custom AI applications or workflows.

1. Can I use MCP without coding?

No, MCP requires technical knowledge like handling APIs and SDKs. No-code users can explore tools like Greta instead.

1. Is MCP the only way to build connected AI agents?

No, it's one of many methods. MCP is powerful for custom control, but other tools offer simpler, out-of-the-box integration options.