Discover the basics of Model Context Protocol MCP, how it works in AI systems, and why developers are using it to link models with real-time tools and data.
Different types of AI models, from large language models to computer vision systems, are capable of supporting a wide range of tasks, including generating text, analyzing images, detecting patterns, and making predictions. However, connecting these models to real-world computer systems in a seamless, scalable way has typically required complex integration efforts.
While a model might perform well on its own, deploying it in practical environments often requires access to external tools, live data, or domain-specific context. Stitching these elements together usually involves custom code, manual setup, and limited reusability.
Recently, the concept of a Model Context Protocol (MCP) has been gaining attention in the AI community. MCP is an open standard that allows AI systems to exchange information with tools, files, and databases using a shared, structured format. Instead of building integrations for every use case, developers can use MCP to streamline how models access and interact with the context they need.
You can think of MCP as a universal adapter. Just like a travel adapter lets your devices plug into different power outlets around the world, MCP lets AI models plug into various systems, tools, and data sources without needing a custom setup for each one.
In this article, we’ll take a closer look at what MCP is, how it works, and the role it plays in making AI more effective in real-world applications. We’ll also explore some real-world examples of where MCP is being used.
Model Context Protocol (MCP) is an open standard created by Anthropic, an AI safety and research company known for building advanced language models. It gives AI models a clear way to connect with tools, files, or databases.
Most AI assistants today rely on large language models to answer questions or complete tasks. However, those models often need extra data to respond well. Without a shared system, each connection must be built from scratch.
For example, a chatbot designed to help with IT support might need to pull information from a company’s internal ticketing system. Without MCP, this would require a custom integration, making the setup time-consuming and difficult to maintain.
MCP solves that problem by acting as a common port for all tools and models. It doesn’t belong to any one company or model - rather, it’s a new concept for how AI systems can connect with external data and services.
Any developer can use MCP to build assistants that work with live information. This reduces setup time and avoids confusion when switching between tools or platforms.
Anthropic introduced the idea of Model Context Protocol (MCP) in November 2024. It started as an open-source project to improve how language models interact with tools and data.
Since then, MCP has gained a lot of attention. It started with developers building internal tools for things like document search and code assistance. That early interest quickly grew, with larger companies beginning to use MCP in their production systems.
By early 2025, support for MCP started spreading across the tech industry. OpenAI and Google DeepMind, two leading AI research labs, announced that their systems would work with the protocol.
Around the same time, Microsoft released tools to help developers use MCP more easily, including support for its popular products like Copilot Studio, which helps businesses build AI assistants, and Visual Studio Code, a widely used code editor.
At the heart of MCP are three main parts: clients, servers, and a shared set of rules called the protocol. Think of it like a conversation between two sides: one asking for information and the other providing it.
In this setup, the AI system plays the role of the client. When it needs something, like a file, a database entry, or a tool to perform an action, it sends a request. On the other side, the server receives that request, grabs the needed information from the right place, and sends it back in a way the AI can understand.
This structure means developers don’t have to build a custom connection whenever they want an AI model to work with a new tool or data source. MCP helps standardize the process, making everything faster, simpler, and more reliable.
Here’s a walkthrough of how MCP connects an AI assistant with external data or tools:
Nowadays, MCP is already being used across a variety of tools and platforms that rely on real-time context. Here are some examples of how companies are using the protocol to connect language models with live systems and structured data:
Next, let’s take a closer look at a branch of AI where MCP is just beginning to emerge: computer vision.
While computer vision models like Ultralytics YOLO11 are great at identifying patterns and objects in images, their insights can become even more impactful when combined with the right context.
In real-world applications, especially in healthcare, adding context like patient history, lab results, or clinical notes can significantly enhance the usefulness of model predictions, leading to more informed and meaningful outcomes.
That’s where the Model Context Protocol (MCP) comes in. While it isn’t widely used yet and is still a developing approach being explored by researchers and engineers, it shows a lot of potential.
For instance, in the diagnosis of diabetic retinopathy, a condition that can cause vision loss in people with diabetes, an AI assistant can use MCP to coordinate multiple specialized tools. It might start by retrieving patient records from a database and assessing diabetes risk using a predictive model.
Then, a computer vision model analyzes retinal images for signs of damage, such as bleeding or swelling, that indicate the presence or severity of retinopathy. Finally, the assistant can search for relevant clinical trials based on the patient’s profile.
MCP enables all of these tools to communicate through a shared protocol, allowing the assistant to bring together image analysis and structured data in one seamless workflow.
Each tool is accessed through an MCP server, which enables the assistant to send structured requests and receive standardized responses. This eliminates the need for custom integrations and enables the assistant to combine image analysis with critical patient data in one smooth, efficient workflow. Although MCP is still new, there’s already a lot of research and ongoing work aimed at making use cases like this practically possible.
Here are some of the key advantages that MCP offers:
On the other hand, here are a few limitations to keep in mind when it comes to MCP:
AI models are becoming more capable, but they still rely on access to the right data. The Model Context Protocol (MCP) offers developers a consistent and standardized way to establish those connections. Instead of building each integration from scratch, teams can follow a shared format that works across different tools and systems.
As adoption grows, MCP has the potential to become a standard part of how AI assistants are designed and deployed. It helps streamline setup, improve data flow, and bring structure to real-world model interactions.
Join our growing community. Visit our GitHub repository to learn more about AI and explore our licensing options to get started with Vision AI. Want to see how it’s used in real life? Check out applications of AI in healthcare and computer vision in retail on our solutions page.
Begin your journey with the future of machine learning
EN
ZH
KO
JA
RU
DE
FR
ES
PT
IT
AR
TR
VI