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.
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.
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:
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.
Imagine you’re chatting with an AI assistant about planning a vacation. You say:
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.
MCP can carry and organize various types of information, including:
This context helps the model understand not just what you’re asking, but why — and that’s where the magic happens.
MCP is built on a client-server model that includes the following main parts:
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.
Aspect | Model Context Protocol (MCP) | AI Agents |
---|---|---|
Definition | A framework for connecting AI models to apps, tools, and data sources | Autonomous AI tools that can make decisions and take actions |
Primary Function | Enables integration and context sharing across tools and workflows | Acts autonomously based on user goals or instructions |
Autonomy | Does not inherently make a model autonomous | Designed to operate with autonomy |
Use Case Example | Connecting a CRM, calendar, and documents to an AI assistant | ChatGPT Deep Research deciding what and where to search online |
Target Users | Developers and technical teams | End-users, businesses, and non-developers |
Technical Requirements | Requires coding knowledge (APIs, SDKs, integration logic) | May or may not require coding, depending on the platform |
Tool Type | Infrastructure and integration protocol | End-user facing, outcome-driven tools |
Flexibility | High customization potential | Limited flexibility in pre-built tools; more in custom agents |
Examples | Custom AI assistants in enterprise apps | Zapier Agents, Greta, AutoGPT, ChatGPT Deep Research |
No-Code Friendly? | No | Yes, if using platforms like Zapier Agents |
Best For | Teams building custom AI features into products | Users needing quick setup for autonomous workflows |
Relationship | Can enable agentic behavior when combined with autonomous logic | May use MCP (or not) as part of their architecture |
Absolutely, privacy is core to the MCP design.
Here’s how it keeps users in control:
You always decide what the AI knows and when. That’s non-negotiable.
If you’re building apps that integrate with AI, MCP is your gateway to:
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.
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.
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.
Not necessarily. MCP enables integration, but autonomy comes from how the AI is built to use that context for decision-making.
MCP is best suited for developers or technical teams building custom AI applications or workflows.
No, MCP requires technical knowledge like handling APIs and SDKs. No-code users can explore tools like Greta instead.
No, it's one of many methods. MCP is powerful for custom control, but other tools offer simpler, out-of-the-box integration options.
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