Using MCP Servers in Cursor: A Complete Guide

If you’ve been exploring Cursor, the AI-powered code editor, you may have come across the term MCP servers. For many developers, this sparks curiosity: What exactly are MCP servers? How do they work inside Cursor? And why should I care?

This guide will walk you through everything you need to know about MCP servers, how to set them up in Cursor, and how they can supercharge your development workflow.

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What are MCP Servers?

MCP stands for Model Context Protocol.
It’s a new open standard designed to let AI tools (like Cursor) talk to external services and data sources in a structured way.

Think of it like this:

• Normally, AI coding assistants are limited to whatever code you’ve opened in your editor.

• With MCP servers, you can plug external tools, databases, APIs, or knowledge sources directly into Cursor so the AI can use them while helping you code.

Example:

• You’re working on a web app.

• Instead of manually checking your database schema, you connect Cursor to a PostgreSQL MCP server.

• Now the AI can query the schema directly and suggest SQL queries tailored to your actual database.

This makes the AI much more context-aware and useful.

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Why Use MCP Servers in Cursor?

Here are a few key benefits:

1. Extended Context – Connect Cursor to databases, APIs, or local tools so it can use them while generating code.

2. Better Accuracy – The AI won’t just “guess”; it will work with real data from your environment.

3. Custom Workflows – You can create MCP servers for your own tools, making Cursor fit your workflow perfectly.

4. Team Productivity – Share MCP server configurations with teammates so everyone benefits from the same connected tools.

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How MCP Works in Cursor

Cursor uses MCP servers as plugins that expand what the AI can do.
Each MCP server follows the Model Context Protocol, which defines:

• Resources → e.g., a database, a file system, an API endpoint

• Tools → actions the AI can call (e.g., “run query”, “fetch document”)

• Messages → structured communication between Cursor and the server

When connected, Cursor can seamlessly call these resources as if they were part of your project.

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Setting Up MCP Servers in Cursor

Here’s a step-by-step guide:

1. Install Cursor

If you haven’t already, download Cursor from cursor.com. It’s available for macOS, Windows, and Linux.

2. Enable MCP

• Go to Settings > Features in Cursor. 

• Look for MCP Integration and enable it.

Visit File->Preferences->Cursor Settings->MCP Tools

3. Configure MCP Servers

MCP servers are usually defined in your cursor.json or config files.
For example, to connect to a PostgreSQL MCP server:

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": {
        "PGHOST": "localhost",
        "PGUSER": "myuser",
        "PGPASSWORD": "mypassword",
        "PGDATABASE": "mydb"
      }
    }
  }
}

Here’s what’s happening:

• postgres → The name of the MCP server.

• command → How to start the server (Node, Python, or other).

• env → Environment variables needed for connection.

4. Restart Cursor

After editing the configuration, restart Cursor. The MCP server should now appear as a connected tool in the AI sidebar.

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Popular MCP Servers You Can Use

Here are some commonly used MCP servers in Cursor:

• PostgreSQL – Lets the AI query your actual database.

• Filesystem – Allows the AI to read/write files beyond just the open project.

• HTTP / API server – Connect to REST APIs or GraphQL endpoints.

• Custom servers – You can build your own MCP server to expose any resource.

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Creating Your Own MCP Server

If you need something custom, you can write your own MCP server.

Example in Node.js:

import { Server } from "@modelcontextprotocol/sdk";

const server = new Server({
  name: "hello-world",
  version: "1.0.0",
  tools: {
    sayHello: {
      description: "Say hello to a user",
      inputSchema: { type: "object", properties: { name: { type: "string" } } },
      handler: async ({ name }) => {
        return { message: `Hello, ${name}!` };
      }
    }
  }
});

server.listen();

Save it, run it, and connect it through your Cursor config. Now Cursor’s AI can call sayHello whenever needed!

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Best Practices

1. Keep Credentials Safe – Use environment variables or secret managers instead of hardcoding passwords.

2. Limit Scope – Only expose what’s necessary through your MCP server.

3. Test Interactions – Verify that Cursor uses the MCP server correctly before relying on it in production.

4. Stay Updated – MCP is evolving quickly, so keep your server libraries up-to-date.

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The Future of MCP in Cursor

MCP servers are still a new but growing ecosystem. Soon, you’ll see:

• A marketplace of MCP servers (ready-to-use for databases, cloud providers, SaaS tools).

• Better UI integration in Cursor for managing servers.

• Teams sharing pre-configured MCP setups for collaborative coding.

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Final Thoughts

MCP servers unlock a whole new level of power in Cursor. Instead of being a passive assistant, the AI can now interact with your actual tools, APIs, and databases in real-time.

Whether you’re a solo developer or part of a team, experimenting with MCP servers can save time, reduce errors, and make your coding assistant much smarter.

If you’re serious about boosting productivity, it’s worth setting up your first MCP server today!

Get this AI Course to start learning AI easily. Use the discount code QPT. Contact me to learn AI, including RAG, MCP, and AI Agents