MCP servers are changing the game for AI tools

Anonymous

Buildcamp Founder

If you’ve heard of MCP servers and wondered what the hype is about—you’re in the right place.

Model Context Protocol (MCP) servers are changing the game for AI tools. They let AI assistants actually do things—not just talk. From querying your Supabase database to making payments with Stripe or managing pull requests on GitHub, MCP servers connect AI models to real-world services.

This beginner-friendly guide explains what MCP servers are, how they work, and how to get started with them inside AI-powered editors like Cursor and Windsurf.

In Plain English: What’s an MCP Server?

MCP stands for Model Context Protocol. It’s a standard that lets AI assistants (like those in Cursor or Windsurf) plug into external tools and services.

Without MCP, an AI might give you code or advice—but it can’t take action.

With MCP, it can:

Talk to your database
Create GitHub issues or review pull requests
Interact with Stripe to handle payments
And a whole lot more

Think of MCP servers as the missing link between text-generation AI and actual software automation.

How MCP Servers Work (The Simple Version)

There are three parts to the system:

MCP Host: This is your AI assistant (like in Cursor or Windsurf)
MCP Client: The thing inside the host that makes requests to tools
MCP Server: External tools or APIs you want the AI to use

MCP servers expose:

Resources (like REST GET endpoints) for reading data
Tools (like POST, PUT, DELETE) to perform actions

Why Use MCP? Here’s What You Get:

✅ Standardized format — so one tool can work across many hosts
✅ Modularity — plug in new tools without changing your AI
✅ Interoperability — one server can work with many LLMs or agents
✅ Security — sandboxed tools, safer actions

And most importantly: AI assistants become way more useful.

MCP vs. Traditional APIs

Traditional APIs:

Are often monolithic
Can be hard to scale
Mix read/write logic in complex ways

MCP:

Uses microservices
Separates read-only resources from tools
Lets you scale and manage services independently
Makes fault isolation and security easier

Comparison	Traditional API	MCP
Architecture	Monolithic	Microservices
Flexibility	Less flexible	Plug-and-play modular
Scaling	Scale entire system	Scale individual services
Tool Access	Usually limited	Purpose-built and exposed

Popular MCP Servers in the Wild

🧠 GitHub MCP

Read repos, issues, pull requests
Comment, commit, and review code
Use AI to manage dev workflows

Prompt example:

"Review the latest PR and flag anything unusual."

🧩 Supabase MCP

Manage your database via natural language
Read/write records, modify schemas, run SQL

Prompt example:

"Show me all users with an @gmail.com email."

💳 Stripe MCP

Create payments, subscriptions, invoices
Manage customers and plans

Prompt example:

"Create a new $9.99/month plan called 'Pro Plan'."

How to Use MCP Servers in Cursor or Windsurf

🔧 In Cursor

Open Command Palette → Search for "MCP settings"
Enable MCP
Add MCP servers manually or via Composio (managed service)

🌊 In Windsurf

Go to Settings → MCP
Enable MCP (on by default for pro users)
Add servers (manually or from catalog)

Supports two types of transport:

stdio (local server)
/sse (remote via HTTPS)

Once added, ask the AI something like:

"Update the Supabase schema to add a last_login timestamp column."

Real Examples

✅ GitHub MCP

"Review PR #456 and leave comments for unclear variable names."

AI authenticates with GitHub
Loads the PR
Leaves inline suggestions

✅ Supabase MCP

"Find users who haven't logged in since Jan 1."

AI creates a SQL query
Runs it
Returns the data in chat

✅ Stripe MCP

"Show me all failed payments from the last 30 days."

AI queries failed charges
Returns a formatted summary

Bonus: Cross-Service Workflows

This is where it gets cool. You can chain actions:

Create a GitHub issue
Link it to a feature branch
Update your Supabase schema
Push to Vercel

All from one prompt. ✨

Best Practices

✅ Use limited-scope tokens
✅ Rotate keys regularly
✅ Avoid using MCP for sensitive data edits (review before applying)
✅ Run local servers for high-trust environments

When to Use MCP

✅ Managing your dev workflow via chat
✅ Writing + running SQL without opening a DB client
✅ Creating and managing SaaS billing flows
✅ Automating boring ops tasks (without building full dashboards)

Skip it when:

You’re making irreversible changes
You need granular approval flows
The AI isn’t confident in the request

Final Thoughts

MCP servers give AI real power: not just to generate, but to execute.

It’s a huge step forward for devs, founders, and power users who want to work faster—and smarter.

Try it in Cursor, Windsurf, or build your own MCP server for any tool or service you love.

You’re not just talking to AI anymore. You’re working with it.

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