What Is The Model Context Protocol Mcp

Bonisiwe Shabane

-Dec 28, 2025, 5:48 PM

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. 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] 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. 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. In November last year, Anthropic introduced an open-source project called the Model Context Protocol (MCP). The announcement didn’t grab headlines at the time, but MCP has since taken off. Now, both OpenAI and Google, the two leading AI labs in the world, have pledged to support the MCP standard. But what is MCP, and what are its applications in AI?

We have explained the Model Context Protocol in detail below to help you understand how it works and its uses. Model Context Protocol (MCP) is an open-source standard developed by Anthropic, the company behind the Claude AI chatbot. MCP allows AI models to connect to external data, read them, and execute actions through a universal connector. You see, AI models are plenty powerful, but they live in isolation and can’t read your files or Slack messages. For AI models to access external data or systems, companies have to build custom connectors for each application. MCP replaces all that with a universal connector (a common protocol) to interact with external data.

For instance, you can use MCP to connect an AI model like Claude with Google Drive or GitHub. With the common MCP protocol, you can use AI models to interact with data sources in a secure and context-aware way. It establishes a two-way connection: one is through the MCP server and another is through the MCP client. For example, the Claude Desktop app is an MCP client that asks for data, and the MCP server is the connector that provides the data. MCP is a developer-centric tool, and developers can build MCP servers and clients. So what is there for end consumers like you and me?

Well, users can install MCP servers for Google Maps, WhatsApp, Slack, Google Drive, GitHub, Bluesky, Windows, macOS, Linux, and more. This will allow you to fetch information from these services in an AI chatbot like ChatGPT. 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. Model Context Protocol (MCP) is a new way to help standardize the way large language models (LLMs) access data and systems, extending what they can do beyond their training data. It standardizes how developers expose data sources, tools, and context to models and agents, enabling safe, predictable interactions and acting as a universal connector between AI and applications. Instead of building custom integrations for every AI platform, developers can create an MCP server once and use it everywhere. MCP connects AI models (like Claude, GPT-4, etc) to external tools and systems.

That can be your app’s API, a product database, a codebase, or even a desktop environment. MCP lets you expose these capabilities in a structured way that models can understand. MCP isn’t a library or SDK. It’s a spec, like REST or GraphQL, but for AI agents. Models continue to rely on their own trained knowledge and reasoning, but now they have access to specialized tools from MCP servers to fill in the gaps. If it reaches limits in its own understanding, it can call real functions, get real data, and stay within guardrails you define instead of fabricating its own answers (hallucinating).

DigitalOcean vs. AWS Lightsail: Which Cloud Platform is Right for You? Model Context Protocol (MCP) has emerged as a hot topic in AI circles. Scrolling through social media, we’ve been seeing MCP posts by explainers, debaters, and memers alike. A quick search on Google or YouTube reveals pages upon pages of new content covering MCP. Clearly, the people are excited.

But about what exactly? Well, it’s quite simple: if models are only as good as the context provided to them, a mechanism that standardizes how this context augmentation occurs is a critical frontier of improving agentic capabilities. For those who have not had the time to dive into this concept, fear not. The goal of this article is to give you an intuitive understanding around the ins and outs of MCP. While this explanation of Model Context Protocol (MCP) aims to be accessible, understanding its role in the evolving landscape of AI applications will be greatly enhanced by a foundational understanding of the capabilities of... Introduced November 2024 by Anthropic as an open-source protocol, MCP allows for the integration between LLM applications and external data sources and tools.

The Model Context Protocol (MCP) is an open source framework that aims to provide a standard way for AI systems, like large language models (LLMs), to interact with other tools, computing services, and sources... Helping generative AI tools and AI agents interact with the world outside themselves on their own is a key to allowing autonomous AI to take on real-world tasks. But it has been a difficult goal for AI developers to realize at scale, with much effort being put into complex, bespoke code that connects AI systems to databases, file systems, and other tools. Several protocols have emerged recently that aim to solve this problem. MCP in particular has been adopted at an increasingly brisk pace since it was introduced by Anthropic in November 2024. With MCP, intermediary client and server programs handle the communication between AI systems and external tools or data, providing a standardized messaging format and set of interfaces that developers use for integration.

In this article, we’ll introduce you to the Model Context Protocol and talk about its impact on the world of AI. Before diving into the details of the different building blocks of MCP architecture, we first need to define the MCP server, since it has become nearly synonymous with the protocol itself. An MCP server is a lightweight program that sits between an AI system and some other service or data source. The server acts as a bridge, communicating with the AI (via an MCP client) in a standardized format defined by the Model Context Protocol, and with the other service or data source via whatever... MCP servers are relatively simple to build. A wide variety of them, which can do anything from interact with a database to get the latest weather reports, are available on GitHub and elsewhere.

The vast majority are free to download and use (though paid MCP servers also are emerging). This is part of what has made MCP so popular so quickly: Developers and users of all sorts of AI systems found that they could use these readily available MCP servers for a variety... And that widespread adoption was made possible by the way that MCP servers connect to the AI tools themselves. MCP isn’t the first technique developed to connect AIs to the outside world. For instance, if you want an LLM to integrate documents that aren’t part of its training data into its responses, you can use retrieval augmented generation (RAG), though that involves encoding the target data...

What Is The Model Context Protocol Mcp

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