If you’ve been following the AI space recently, you’ve probably heard the term Model Context Protocol (MCP) being tossed around. It’s one of those buzzwords that keeps showing up in developer discussions, conference talks, and product announcements.
But what exactly is MCP? Why was it introduced? And why are people so excited about it? More importantly why does it matter to developers, tool providers, and AI enthusiasts alike?
Welcome to the first article in my three-part series on MCP. In this series, I will cover the fundamentals of MCP and how to get started, popular MCPs and how to use it in daily work, and finally on how to build your own MCP server.
The Problem Before MCP
Let’s start with the old way.
Imagine you’re building an AI agent that needs to interact with Slack. You’d want it to post updates, respond to messages, or trigger workflows. Without MCP, you’d have to:
Write a wrapper around Slack’s API
Handle all the request/response logic yourself
Repeat the same for every other tool you wanted to integrate (GitHub, Notion, Jira, Google Drive, etc.).
Every developer was reinventing the wheel. Worse, every consumer of these integrations needed their own custom logic to “teach” their model how to use these APIs. It was slow, inconsistent, and painful to scale.
Enter MCP (Our Hero)
To address this, Anthropic (the team behind Claude) introduced the Model Context Protocol. MCP is an open standard that defines how AI models, clients, and external tools can interact cleanly.
The idea is simple but powerful:
Instead of writing custom wrappers, a provider can expose an MCP server.
An MCP client (like an IDE, an AI assistant, or another tool) connects to the server and fetches what’s available.
The model then uses this connection to call functions directly, without needing bespoke glue code
Okay, How MCP Works?
Think of MCP as a three-part ecosystem:
MCP Server: Exposes a set of functions or tools. This could be Slack, GitHub, Notion, or even a custom app you’ve built.
MCP Client: Acts as the bridge between the model and the server. Clients could be Cursor, GitHub Copilot, or Claude desktop apps — essentially anything that embeds a model and wants to extend its capabilities.
The Model: Once connected, the model can “see” what functions are available and use them on demand.
This design means once a model is connected to an MCP server, it can instantly fetch all available functions — no manual integration required.
MCP Is a Spec, Not the API Itself
To put it simply, MCP is to agents what OpenAPI spec is to APIs.
Think about OpenAPI for a second: it doesn’t give you the API itself. Instead, it’s a contract — a structured way of saying, “Here are the endpoints, here’s what they accept, here’s what they return.” It’s not the server, not the playground, not the implementation — just the map that tells you what’s out there.
MCP plays the same role, but in the agent world. An MCP server doesn’t expose the tool directly for you to call like a REST API. Instead, it exposes a spec: “Here are the tools I provide, here’s how they work.” It’s then up to the model (or the agent framework you’re using) to read that spec, decide which tool fits the problem, and call it correctly.
So, instead of thinking of MCP as an “API you hit,” it’s better to think of it as the blueprint that makes agent-tool interaction consistent, interoperable, and portable.
And Why the Buzz?
MCP is attracting attention because it addresses a foundational problem for AI: tool interoperability at scale.
Here’s why it matters:
- No more duplicate wrappers → Providers can expose their tools once, and any client can consume them.
- Discoverability built in → Models can dynamically fetch and understand what tools are available.
- Standardization → A common protocol lowers the barrier to integrating tools.
- Scalability → Imagine hundreds of MCP servers; an AI client could connect to all of them without bloated, custom integrations.
- Openness → It’s not limited to big providers. Anyone can create an MCP server, from a personal project to enterprise-grade software.
If this sounds exciting, you’re not alone — MCP adoption is growing quickly. It could become a foundational piece of how AI systems interact with the world. Just like HTTP unlocked the modern web, MCP could unlock the next generation of AI-powered applications.
And the best part? It’s open, extensible, and still evolving — which means we’re just at the beginning.
This post was all about the fundamentals and the "why" behind MCP. In my next one, I’ll walk through how to get started with MCP and some popular MCP servers you can already try out — from developer tools to productivity apps — and how to connect them in your own IDEs.
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