Model Context Protocol Wikipedia

Bonisiwe Shabane

-Dec 28, 2025, 5:56 PM

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] Get started with the Model Context Protocol (MCP)

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: 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. 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 that enables seamless communication between Large Language Models (LLMs) and external tools, data sources, and services. This document provides a high-level overview of MCP, its core architecture, and how it facilitates structured interactions between AI models and the digital world around them. For detailed information about the implementation architecture, see MCP Architecture, and for specific SDK documentation, refer to SDK Overview. MCP serves as a standardized interface—similar to a "USB-C port for AI applications"—that connects LLMs to various data sources and tools while maintaining a consistent protocol for these interactions.

It addresses the fundamental challenge of providing context to LLMs and allowing them to interact with external systems in a structured, secure manner. MCP follows a client-server architecture with the following key components: The MCP system consists of multiple interconnected components that work together to enable LLMs to interact with external data and tools. Anthropic's Model Context Protocol (MCP) is gaining massive traction as a game-changing standard for connecting Large Language Models (LLMs) to external data sources and tools. Let's take a deep dive into just exactly what MCP is, and how you can begin using it in your projects. MCP is an open protocol that standardizes how applications provide context to LLMs.

Think of MCP like a "USB-C port for AI applications". Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to external data sources and tools. The protocol follows a client-server architecture with three main components: A Model Context Protocol (MCP) server that retrieves information from Wikipedia to provide context to Large Language Models (LLMs). This tool helps AI assistants access factual information from Wikipedia to ground their responses in reliable sources. The Wikipedia MCP server provides real-time access to Wikipedia information through a standardized Model Context Protocol interface.

This allows LLMs to retrieve accurate and up-to-date information directly from Wikipedia to enhance their responses. The best way to install for Claude Desktop usage is with pipx, which installs the command globally: This ensures the wikipedia-mcp command is available in Claude Desktop's PATH. To install wikipedia-mcp for Claude Desktop automatically via Smithery:

People Also Search

A practical introduction to the Model-Context-Protocol (MCP)

As LLMs and AI agents grow increasingly powerful, they face a critical limitation: accessing up-to-date information and specialized tools in a consistent, standardized way. The Model-Context-Protocol (MCP), developed by Anthropic, addresses this challenge by creating a unified interface between AI models and external resources. This standardization eliminates the fragmentation of custom integratio...