Mcp 101 An Introduction To Model Context Protocol

Bonisiwe Shabane

-Dec 28, 2025, 5:47 PM

mcp 101 an introduction to model context protocol

Get started with the Model Context Protocol (MCP) Introduction to Model Context Protocol (MCP) and get access to the augmented documentation experience Welcome to Unit 1 of the MCP Course! In this unit, we’ll explore the fundamentals of Model Context Protocol. By the end of this unit, you’ll have a solid understanding of the foundational concepts of MCP and be ready to dive deeper into its architecture and implementation in the next unit.

The AI ecosystem is evolving rapidly, with Large Language Models (LLMs) and other AI systems becoming increasingly capable. However, these models are often limited by their training data and lack access to real-time information or specialized tools. This limitation hinders the potential of AI systems to provide truly relevant, accurate, and helpful responses in many scenarios. 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 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] 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. Enjoy unlimited growth with a year of Coursera Plus for $199 (regularly $399). Save now. This course is part of multiple programs. Learn more

You need a solid understanding of basic JavaScript to complete this course. Not there yet? Try Scrimba's Learn JavaScript course here on Coursera. You need a solid understanding of basic JavaScript to complete this course. Not there yet? Try Scrimba's Learn JavaScript course here on Coursera.

Connect AI models to tools and data using Model Context Protocol. As AI systems evolve from simple chat interfaces to sophisticated agents, they face a fundamental challenge: how to securely and efficiently access the vast ecosystem of data sources and tools they need to be... Traditional approaches create fragmented, vendor-locked solutions. MCP solves this with a universal interface standard - think of it as the “HTTP for AI context integration.” Model Context Protocol is an open standard that defines how AI applications should communicate with external resources. Rather than each AI tool creating custom integrations, MCP provides:

Like USB-C standardized device connections, MCP standardizes AI-to-resource connections. One protocol, infinite possibilities. Based on your role and experience level, here’s how to get the most value from MCP: Model Context Protocol (MCP) by Anthropic is an open specification proposed by Anthropic, which enables AI models, agents, and supporting infrastructure to share and manage context. MCP defines a set of message formats and APIs that formalize how context, which can include state, instructions, or data, is communicated between language models, intermediary servers, gateways, and connected services. The aim is to make the entire process of prompt composition, tool use, and multi-agent orchestration reliably interoperable, regardless of back-end or deployment environment.

This protocol goes beyond simple API requests by supporting complex, evolving scenarios such as multi-step workflows, tool calling, and persistent memory across user sessions. MCP’s formalized approach helps developers build systems where context can move fluidly between components, reducing friction and enabling richer, more capable AI applications. It is a foundational standard for scalable agentic systems where consistent context flow is critical for maintaining state, intent, and utility over time. ⬇️ Download the Obot open-source gateway on GitHub and begin integrating your systems with a secure, extensible MCP foundation. Understanding the value of MCP requires looking at how context handling shapes the behavior and usefulness of AI systems. As models grow more capable and workflows become more complex, consistent and interoperable context management becomes essential:

Mcp 101 An Introduction To Model Context Protocol

People Also Search

Get Started With The Model Context Protocol (MCP) Introduction To

The AI Ecosystem Is Evolving Rapidly, With Large Language Models

Scrolling Through Social Media, We’ve Been Seeing MCP Posts By

The Goal Of This Article Is To Give You An

In December 2025, Anthropic Donated The MCP To The Agentic