University of Kwazulu-Natal

What Is Model Context Protocol Mcp And How Does It Work

Bonisiwe Shabane
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what is model context protocol mcp and how does it work

Large language models (LLMs) are powerful, but they have two major limitations: their knowledge is frozen at the time of their training, and they can't interact with the outside world. This means they can't access real-time data or perform actions like booking a meeting or updating a customer record. The Model Context Protocol (MCP) is an open standard designed to solve this. Introduced by Anthropic in November 2024, MCP provides a secure and standardized "language" for LLMs to communicate with external data, applications, and services. It acts as a bridge, allowing AI to move beyond static knowledge and become a dynamic agent that can retrieve current information and take action, making it more accurate, useful, and automated. The MCP creates a standardized, two-way connection for AI applications, allowing LLMs to easily connect with various data sources and tools.

MCP builds on existing concepts like tool use and function calling but standardizes them. This reduces the need for custom connections for each new AI model and external system. It enables LLMs to use current, real-world data, perform actions, and access specialized features not included in their original training. The Model Context Protocol has a clear structure with components that work together to help LLMs and outside systems interact easily. The LLM is contained within the MCP host, an AI application or environment such as an AI-powered IDE or conversational AI. This is typically the user's interaction point, where the MCP host uses the LLM to process requests that may require external data or tools.

Model Context Protocol (MCP) is a standardized framework developed by Anthropic and was introduced in November 2024. It enables AI models to seamlessly connect with external tools and data sources without requiring custom integrations for each platform. By serving as a universal protocol, MCP ensures that AI applications can access real-time, contextually relevant data in a secure, scalable and efficient way. MCP's architecture is designed to be both simple and flexible which helps in enabling good interaction between AI models and various data sources. It works by connecting three key components: MCP Servers, MCP Clients and MCP Hosts. When building AI agents, there are usually three types of context they need to handle:

MCP helps manage all these different types of context clearly and efficiently by: Here we are implementing a Hugging Face MCP server in VS Code using Copilot to enable direct interaction with Hugging Face models and datasets. This setup allows VS Code to send and receive MCP actions like model search and inference through a standardized API connection. Posted on Dec 27 • Originally published at pockit.tools If you've been building AI applications in 2025, you've probably hit the same wall everyone else has: your LLM is brilliant at generating text, but connecting it to real-world data and tools feels like... Enter the Model Context Protocol (MCP)—an open standard that's quietly becoming as fundamental to AI development as REST APIs are to web development.

Originally developed by Anthropic and now adopted across the industry, MCP is solving one of the biggest headaches in AI engineering: how do you give your AI agent reliable, structured access to the outside... In this comprehensive guide, we'll explore what MCP is, why it matters, how it works under the hood, and most importantly—how to implement it in your own AI applications. Before diving into MCP, let's understand the pain it addresses. The Model Context Protocol (MCP) is an open standard and open-source framework introduced by Anthropic in November 2024 to standardize the way artificial intelligence (AI) systems like large language models (LLMs) integrate and share... MCP was announced by Anthropic in November 2024 as an open standard[5] for connecting AI assistants to data systems such as content repositories, business management tools, and development environments.[6] It aims to address the... Earlier stop-gap approaches—such as OpenAI's 2023 "function-calling" API and the ChatGPT plug-in framework—solved similar problems but required vendor-specific connectors.[7] MCP re-uses the message-flow ideas of the Language Server Protocol (LSP) and is transported over...

In December 2025, Anthropic donated the MCP to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, co-founded by Anthropic, Block and OpenAI, with support from other companies.[9] The protocol was released with software development kits (SDKs) in programming languages including Python, TypeScript, C# and Java.[8][10] Anthropic maintains an open-source repository of reference MCP server implementations for enterprise systems.[citation needed] MCP is a way to democratize access to tools for AI Agents. In this article we cover the fundamental components of MCP, how they work together, and a code example of how MCP works in practice. As the race to move AI agents from prototype to production heats up, the need for a standardized way for agents to call tools across different providers is pressing. This transition to a standardized approach to agent tool calling is similar to what we saw with REST APIs.

Before they existed, developers had to deal with a mess of proprietary protocols just to pull data from different services. REST brought order to chaos, enabling systems to talk to each other in a consistent way. MCP (Model Context Protocol) is aiming to, as it sounds, provide context for AI models in a standard way. Without it, we’re headed towards tool-calling mayhem where multiple incompatible versions of “standardized” tool calls crop up simply because there’s no shared way for agents to organize, share, and invoke tools. MCP gives us a shared language and the democratization of tool calling. One thing I’m personally excited about is how tool-calling standards like MCP can actually make AI systems safer.

With easier access to well-tested tools more companies can avoid reinventing the wheel, which reduces security risks and minimizes the chance of malicious code. As Ai systems start scaling in 2025, these are valid concerns. As I dove into MCP, I realized a huge gap in documentation. There’s plenty of high-level “what does it do” content, but when you actually want to understand how it works, the resources start to fall short—especially for those who aren’t native developers. It’s either high level explainers or deep in the source code. In this piece, I’m going to break MCP down for a broader audience—making the concepts and functionality clear and digestible.

If you’re able, follow along in the coding section, if not it will be well explained in natural language above the code snippets. In this guide, you’ll learn how the Model Context Protocol (MCP) works and how to set it up. We’ll go through the steps of building your first server to make it all function properly. You can also check out our guide to using the OpenAI API with Python. Model Context Protocol (MCP) is a structured system that lets language models perform actions through external tools or services. Instead of just responding based on trained knowledge, the model can make a request, ask for something to be done, and use the result in its reasoning.

So, here’s a breakdown of what’s happening behind the scenes when a model uses MCP. Autonomous Multi-Agent Platform in Your Cloud Connect Scattered Data Into Clear Insight Automate Repetitive Tasks and Data Flows Deploy Context-Aware AI Applications at Scale Interact with Your Data using Natural Language

Launch Week is here! Catch up with our latest updates to simplify customer and agentic identity. Let's go > Large language models (LLMs) like Claude, ChatGPT, Gemini, and Llama have completely changed how we interact with information and technology. They can write eloquently, perform deep research, and solve increasingly complex problems. But while typical models excel at responding to natural language, they’ve been constrained by their isolation from real-world data and systems.

The Model Context Protocol (MCP) addresses this challenge by providing a standardized way for LLMs to connect with external data sources and tools—essentially a “universal remote” for AI. Released by Anthropic as an open-source protocol, MCP builds on existing function calling by eliminating the need for custom integration between LLMs and other apps. This means developers can build more capable, context-aware applications without reinventing the wheel for each combination of AI model and external system. This guide explains the Model Context Protocol’s architecture and capabilities, how it solves the inherent challenges of AI integration, and how you can begin using it to build better AI apps that go beyond... It’s no secret that LLMs are remarkably capable, but they typically operate in isolation from real-world systems and current data. This creates two distinct but related challenges: one for end users, and one for developers and businesses.

You focus on building. We’ll keep you updated. Get curated infrastructure insights that help you make smarter decisions. Artificial Intelligence (AI) and Large Language Models (LLMs) have taken the world by storm over the past few years. Even though more powerful models emerge every few months, organizations still struggle to integrate them successfully with their own proprietary data. LLMs are great at writing, reasoning, and analyzing, but they remain trapped behind information silos and legacy systems.

The Model Context Protocol (MCP) represents a significant shift in how AI systems interact with enterprise data and tools. MCP addresses the growing complexity of AI integration by providing a standardized method for LLMs to interact with external systems. The Model Context Protocol (MCP) is an emerging standard for sharing context between AI models and applications. It allows systems to persist and exchange structured information, such as user preferences, app history, or task state, across model interactions or even between models. MCP is designed to improve continuity, memory, and personalization using machine-readable schemas and standardized data exchange formats. It aims to enable seamless, stateful experiences across AI tools and platforms.

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