Understanding MCP Servers: The Brains Behind AI Agents Like Cursor and WindSert

On Kohminds Blog, we explore the hype behind emerging technologies and development paradigms. Today, we're diving into something you've probably heard thrown around lately MCP Servers and breaking down what all the buzz is about in a way even software engineers can finally say, "Ah, now that makes sense!"

If you're someone who's seen tools like Cursor, WindSert, or any other multi-agent AI doing everything from installing dependencies to writing clean, complete codebases and you've asked yourself, how the heck is this even possible? this article is for you.

We're going to explore what an MCP server actually is, how it works behind the scenes, and even walk you through building your own basic one from scratch.

Why LLMs Alone Aren't Enough

Before we understand MCP servers, let's clarify what LLMs (Large Language Models) actually are.

At their core, LLMs are just advanced prediction machines. They don't "understand" things the way humans do. What they do is:

• Predict the next word in a sentence
• Generate text based on context
• Imitate reasoning patterns

But here's the limitation:

• An LLM can write an SQL query, but it can't run it.
• It can generate an email, but it can't send it.
• It can suggest a fix for your code, but without access to your repo, logs, or chat history, it's just guessing.

That's where MCP (Model Context Protocol) servers come in.

What is the Model Context Protocol (MCP)?

Let's break the term down:

• Model: The AI or LLM itself.
• Context: Everything the model needs to know tools, files, resources, etc.
• Protocol: A standardized way of communicating between the model and external systems.

The MCP server acts as a context broker. It connects the AI to the tools, APIs, and systems it needs to actually do things, not just talk about doing them.

What's Inside an MCP Context?

A context may include:

1. Tools – Functions the AI can call to perform real-world actions (e.g., fetch data from a DB, send an email).
2. Resources – Files, logs, or other data the AI should "see".
3. Sampling – Ways to query multiple models and route tasks to the most suited one.
4. Prompts – Templates or augmentations that improve or reshape the user's prompt.

Let's Make it Real: Cursor, WindSert & Slack Integration

Imagine this scenario:

you’re a developer trying to fix a bug in your codebase. You’ve been staring at the error for a while, and tools like Cursor or Copilot can suggest code changes but those suggestions lack real context. They don’t know why the code was written this way or what decisions were made during a team discussion last week.

Now, think about that Slack conversation your team had the one where someone explained the rationale behind the architecture decision, or shared a workaround for a known limitation. That conversation holds critical context for understanding the bug.

Here’s where an MCP (Model Context Protocol) server comes in.

An MCP server acts as a middleware between the AI model and external data sources like Slack. When the AI is trying to solve a problem, it can invoke tools defined by the MCP server for example, a tool that fetches recent messages from a relevant Slack channel.

This enables the AI to:

• Query real-time data (e.g., search Slack messages for keywords related to the error)
• Analyze the history of discussions (e.g., find a teammate’s earlier explanation)
• Build a full picture of what’s happening not just from code, but from human context
• Generate a solution that aligns with your team’s actual decisions, not just best guesses

In short, the AI moves from being a generic code assistant to an intelligent teammate one that can search, reason, and act using tools and data specific to your environment.

MCP Servers Introduce a New Programming Paradigm

Just like the rise of HTTP servers changed how we build software, MCP servers represent a shift in how we architect intelligent systems.

Instead of stateless endpoints, you now have AI agents that:

• Call tools
• Pull live context
• Act based on protocols
• Collaborate across models

Want to Build One? Here's a Simple MCP Server with MongoDB

Below is a minimal example using the official MCP SDK to build a server that lets your AI fetch data from a MongoDB collection.

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { MongoClient } from "mongodb";

// MongoDB connection
let db;

async function connectDB() {
  const client = new MongoClient("mongodb://localhost:27017");
  await client.connect();
  db = client.db("demo");
}

const server = new Server({
  name: "mongodb-server",
  version: "1.0.0",
});

// Simple fetch operation
server.addTool({
  name: "fetch_data",
  description: "Fetch documents from MongoDB collection",
  inputSchema: {
    type: "object",
    properties: {
      collection: { type: "string" }
    },
    required: ["collection"]
  }
}, async (args) => {
  const documents = await db.collection(args.collection).find({}).toArray();
  return { content: [{ type: "text", text: JSON.stringify(documents, null, 2) }] };
});

// Start server
async function main() {
  await connectDB();
  const transport = new StdioServerTransport();
  await server.connect(transport);
}

main();

In this example, the AI could call the fetch_data tool, passing in a collection name, and the server would return real documents from your database. Simple, but powerful.

Learn More

• Anthropic's official announcement about Model Context Protocol

• MCP App Store – mcpappstore.com: Think of it as an App Store for MCP tools.

• @modelcontextprotocol/sdk: Official SDK to get started with your own servers

Final Thoughts

MCP isn't just a trend it's a foundational shift in how we extend AI beyond pure language generation. It's how you bridge the gap between "what AI can say" and "what AI can do".

If you're a developer, this is your opportunity to build the next generation of tools, services, and agents that work with AI as collaborators, not just assistants.

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