Inside the MCP Revolution: How AI Systems Are Learning to Speak the Same Language 0 like April 22, 2025 Share this post Last Updated on April 22, 2025 by Editorial Team Author(s): Harshit Kandoi Originally published on Towards AI. Photo by Gerard Siderius on Unsplash Imagine a network of AI systems consisting of virtual assistants, recommendation engines, and robotic agents, all working on their own. But not “in sync”. Each time you interact with one, you have to start from scratch, unaware of your prior choices, recent interactions, or even the idea on which it operates. The result? Unnecessary processes, inconvenient experiences, and missed the chance to enjoy true machine automation. This is the price we have to pay for context loss, and it’s become a pressing challenge in today’s AI-driven world. Let’s Enter the World of Model Context Protocol (MCP), an innovative way that promises to restructure how AI systems interact and collaborate. MCP is a standardized framework created to allow the sharing of contextual data across models, ensuring continuity, coherence, and connectivity in these complex AI ecosystems. Why does this matter now, compared to ever before? As we know, AI becomes more embedded in everything from health services to autonomous systems, the need for intelligent context-sharing is not just a technical convenience, but it’s a fundamental requirement. Without it, even the most powerful AI models operate in silos, unable to utilise collective knowledge or maintain user continuity. In this blog, we’ll… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post