????#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

everything you need to know about Model Context Protocol

“Even the most sophisticated models are constrained by their isolation from data – trapped behind information silos and legacy systems.”

?Anthropic, on why context integration matters

Large language models (LLMs) today are incredibly smart in a vacuum, but they struggle once they need information beyond what’s in their frozen training data. For AI agents to be truly useful, they must access the right context at the right time – whether that’s your files, knowledge bases, or tools – and even take actions like updating a document or sending an email based on that context. Historically, connecting an AI model to all these external sources has been a messy, ad-hoc affair. Developers had to write custom code or use specialized plugins for each data source or API. This made “wire together” integrations brittle and hard to scale.

To simplify that, Anthropic came up with Model Context Protocol (MCP) – an open standard designed to bridge AI assistants with the world of data and tools, to plug in many different sources of context. They announced it in November 2024. The reaction was sort of blah. But now MCP is trending, already passing Langchain and promising to overcome OpenAPI and CrewAI pretty soon. Major AI players and open-source communities are rallying around MCP, seeing it as a potential game-changer for building agentic AI systems. Why?

In this article, we’ll dive deep into MCP – why it’s a hot topic right now, how MCP enables the shift toward more integrated, context-aware AI, its place in agentic workflows, and the under-the-radar details that developers, researchers, AI engineers, and tech executives should know. We’ll also explore some innovative applications of MCP that few have attempted. Overall, it’s a great starting guide, but also useful for those who have already experimented with MCP and want to learn more. Dive in!

What’s in today’s episode?

• Why Is MCP Making Waves Now (and Not Last November)?
• So, What Is MCP and How Does It Work?
• Before MCP, How Were AI Systems Handling Context And Tool Access?
• Is MCP a Silver Buller and Solve-It-All?
• MCP in Agentic Orchestration and Its Place in the Agentic Workflow
• New Possibilities Unlocked by MCP
• Concluding Thoughts
• Resources to dive deeper

Why Is MCP Making Waves Now (and Not Last November)?

MCP was first open-sourced and announced by Anthropic in late November 2024. At the time, it was an exciting idea but not that many noticed it and took seriously. It’s in early 2025 that MCP has really surged into the AI community’s consciousness. There are a few big reasons for this recent buzz:

• Integration Problem Solver: AI agents and agentic workflows became major buzzwords in 2023–2024, but their Achilles’ heel remained: integrating these agents with real-world business systems and data. Initially, much attention went to model capabilities and prompt techniques, not integration. MCP squarely addresses this gap by defining “how to connect existing data sources” (file systems, databases, APIs, etc.) into AI workflows. As people digested this, MCP started to be seen as the missing puzzle piece for serious, production-ready AI agents. (That’s one of the takes from HumanX conference: In recent years, we've primarily been focused on building individual AI models, each specialized for specific tasks. But as complexity and demands grow, a shift is happening towards integrated systems – orchestrations of multiple specialized models, software components, APIs, data sources, and interfaces working cohesively.)
• Community and Adoption: In just a few months, MCP went from concept to a growing ecosystem. Early adopters included companies like Block (Square), Apollo, Zed, Replit, Codeium, and Sourcegraph, who began integrating MCP to enhance their platforms. Fast forward to 2025, and the ecosystem has exploded – by February, there were over 1,000 community-built MCP servers (connectors) available. Clearly, MCP has struck a chord as the industry moves toward more integrated and context-aware AI. This network effect makes MCP even more attractive: the more tools available via MCP, the more useful it is to adopt the standard.
• De Facto Standard Momentum: Unlike yet another proprietary SDK or one-off framework, MCP is open and model-agnostic, and it’s backed by a major AI player. This means any AI model (Claude, GPT-4, open-source LLMs, etc.) can use MCP, and any developer or company can create an MCP integration without permission. Many in the community now see MCP as the likely winner in the race to standardize how AI systems connect to external data (much like how USB, HTTP, or ODBC became ubiquitous standards in their domains).
• Rapid Evolution and Education: Anthropic didn’t just release MCP and walk away; they have been actively improving it and educating developers. During the recent AI Summit, Anthropic’s Mahesh Murthy delivered a workshop that went viral, accelerating MCP adoption. (Remember, all links for further learning are included at the end of the article.)

So, What Is MCP and How Does It Work?

Read further on Hugging Face