The AI revolution isn’t just about chat, it’s about getting things done.

We’ve seen what LLMs can do: they write poems, answer coding questions, summarize papers, even roleplay a pirate therapist :). But let’s be honest so far, they’re mostly just clever talkers. Impressive? Sure, but passive.

The real magic happens when AI stops chit chatting and starts execution stuff.

Imagine giving your AI the power to:

• Pull real-time stock prices
• Clean up a messy spreadsheet
• Auto-tag customer tickets
• Or sync Jira with Notion without crying — try it (it’s worth it)

That’s what MCP (Model Context Protocol) unlocks. It’s basically the USB-C port for AI a universal, standardized way to plug your LLM into the real world. Your APIs, your tools, your data. You expose capabilities; the AI calls them like a polite guest who read the manual.

Think of it like giving your AI agent superpowers. Instead of “Can I help you with anything else today?” it’s now:
“Want me to update your Trello board, check your Stripe balance, and book a Zoom call? Done.” And here’s the kicker: barely anyone is building this right now.

If the chatbot boom was Gen-1, MCP is Gen-2, the “app store” moment for AI agents. Only, it hasn’t gone mainstream yet. Which means if you start now, you’re early.

In this guide, you’ll learn how to:

• Understand how MCP actually works (without losing your sanity),
• Build your own MCP server using Python or TypeScript,
• Connect it with real AI agents (Claude, Cloudflare, MindsDB),
• And monetiit, it. Yes! Even as a side project.

Ready to move from talk to action?

So what is MCP, actually?

Let’s ditch the jargon for a sec.

MCP (Model Context Protocol) is how large language models like Claude go from “text-only” geniuses to full-on digital assistants that can run tasks, fetch data, and interact with tools.

The 3 key building blocks of MCP

MCP exposes 3 main types of things to your AI agent:

1. Tools Think “commands” the AI can run

You define functions like grab_price, send_email, or clean_csv.
The AI sees them as capabilities. It figures out when and how to call them.
Like giving your AI a Swiss Army knife and it actually knows which blade to use.

Example:

tool("grab_price", async ({ input }) => {
  return await getPriceFromSomeAPI(input.productId);
});

2. Resources Think structured data the AI can read/write

Let’s say you have a config file, a user profile, or a knowledge base.
Expose those as REST-like paths (config://version, users://1234/profile) and the AI can interact with them intelligently.

It’s like exposing your database… but in AI-speak.

3. Prompts Mini scripts or instructions

You can feed your AI extra context like cheat codes.
Example: summarize_feedback, prioritize_tasks, etc.
Instead of reinventing the prompt wheel, MCP lets you store and reuse patterns.

Analogy time (because why not):

MCP is like giving your AI a smartphone:

Apps = tools

Local storage = resources

Shortcuts = prompts

Without it, your AI is like a bored genius locked in a room.
With it? You’ve got an assistant that knows how to send invoices, analyze Excel sheets, and automate your workflow before you even open your laptop.

Next up: why you should care, especially if you’re a developer looking for that next niche, profitable side project, or just cool stuff to build on weekends.

Why devs should care this is your “iPhone App Store” moment

Let’s be blunt: MCP isn’t just another protocol. It’s the blueprint for how AI agents will actually interact with the world, and you get to build what they use.

Remember when mobile devs made bank building fart apps and flashlight toggles in the early days of the iPhone? We’re there again, except now it’s with AI tools that talk to APIs, clean data, and execute actions like a virtual assistant on steroids.

Devs are sitting on painkiller problems (you probably have one too, add in comments if you do?)

Here’s the vibe:
The world is full of workflows that suck (sadly). Most of them look like this:

• Copy-paste from X to Y
• Log in → export → clean → upload
• Summarize feedback manually
• Sync tool A with tool B

Now imagine a world where Claude (or any LLM) can say:

“Sure! I’ll fetch competitor prices from 3 sites, clean up the spreadsheet, and update your Notion page before your 9am.”

Boom. That’s an agent powered by MCP tools you built.

Examples of “weekend projects” that could earn you money:

• scrape_jobs: Searches 5 job boards with filters, returns cleaned results.
• summarize_support_tickets: Converts Zendesk chaos into digestible trends.
• sync_jira_notion: Links tasks across teams without tears.
• competitor_price_watcher: Sends daily email alerts if competitors change pricing.

All of these are:

• Low competition
• Immediate business value
• Easy to plug into Claude with MCP

Monetization: build once, charge forever

The beauty of MCP is that it already plays nicely with things like:

• Stripe add a paywall to your MCP endpoint (per request, usage-based, or monthly).
• Cloudflare Workers deploy globally, scale without crying.
• Anthropic’s future “Integrations” tab think early-stage app store for AI.

You could:

• Charge $5/month for your “CSV cleaner for Claude” tool.
• Offer 100 free requests, then switch to Stripe metering.
• Or go enterprise and plug into workflows via Slack + Claude agents.

And here’s the part no one’s saying loudly enough:

MCP is early. Really early.
If you start building now, you’re not late you’re day 1.

How to build your own MCP server (the fast and the functional)

Alright, so you’re sold. You want to build a tool your AI agent can use.
Time to strap in and ship your first MCP server.

There are two solid routes depending on your vibe:

Option A: FastMCP (Python-first, batteries included)

Best for: Python devs who want fast local builds or hobby tools.

Quick setup:

pip install fastmcp

(Or use uv pip install fastmcp if you’re in the cool crowd.)

Starter code:

from fastmcp import FastMCP

mcp = FastMCP("My Cool MCP Server")@mcp.tool()
def add(x: int, y: int) -> int:
    return x + ymcp.run()

This exposes a simple add tool your AI agent can now call like an API.

Adding resources:

@mcp.resource(path="config://version")
def version():
    return {"version": "1.0.0"}Context-aware tool:

@mcp.tool()
def echo_with_user(ctx, message: str) -> str:
    return f"{ctx.user_id} said: {message}"

Deploying:

• Local STDIO: just run the script connects via stdin/stdout.
• HTTP mode: install streamable-http, then mcp.run(http=True)

Test with:

• Claude Desktop
• mcp-remote or fastmcp client for local ping

Option B: Cloudflare Workers (TypeScript + Serverless FTW)

Best for: Web devs, scale nerds, and anyone who wants zero infra.

Setup:

npm create cloudflare@latest
# Choose → my-mcp-server
# Use → cloudflare/ai/demos/remote-mcp-authless template

Sample init.ts snippet:

export default {
  tools: {
    grab_price: async ({ input }) => {
      return await fetchPrice(input.url);
    },
  }
};

This lets Claude (or any agent) call grab_price via MCP.

Deploy:

npx wrangler deploy

And boom you’ve got a live MCP server, ready to take requests globally.

Can’t pick one? Ask yourself:

• Python = great for data tasks, local automation, quick protos
• Cloudflare = great for global reach, fast APIs, web-native tools

Finding a problem to solve = your edge

Here’s a trick:
Go scroll /r/Notion, Twitter/X, or dev forums and look for pain points.

If someone’s complaining about:

• Repetitive workflows
• Zapier being overpriced
• Spreadsheet hell

You’ve likely just found an MCP tool waiting to be built.

Next up: real-world inspiration from open-source MCP servers already out there (MindsDB, Stagehand, Jupyter, GitHub, and more).

Learn from the greats powerful MCP servers already out there

Before you go building yet another “Hello World” calculator for AI…
why not look at what’s already working in the wild?

There are already some killer MCP servers open source, production-grade, and frankly underused.

These aren’t just “examples” you can clone them, customize them, and learn from them.

1. MindsDB the AI data whisperer

What it is: An open-source AI database that also acts as an MCP server.

What it does:

• Connects to 200+ sources: MySQL, MongoDB, Slack, Gmail, Notion, Google Sheets…
• Turns your messy biz data into an AI-readable knowledge base.
• Supports SQL and natural language querying.

Use case:

“Hey Claude, summarize all feedback from negative Trustpilot reviews for product X.”

Install:

pip install mindsdb
# or use the Docker image

https://github.com/mindsdb/mindsdb

2. Stagehand (Browserbase) AI’s headless browser buddy

What it is: A web browsing MCP server with scraping capabilities.

Why it matters:
LLMs + browsing = goldmine. Claude can now:

• Scrape Amazon for prices
• Grab article headlines
• Extract structured data from a page

Use case:

“Find top 10 AI podcasts from Apple’s site and return name + rating.”

https://github.com/browserbase/stagehand

3. Jupyter MCP code meets language model

What it is: Lets LLMs talk directly to Jupyter notebooks.
Yes, really.

Why you care:
Claude can now:

• Analyze CSVs
• Run data science pipelines
• Summarize outputs

Use case:

“Analyze this CSV of Shopify sales and flag unusual dips.”

https://github.com/ai-js/jupyter-mcp

4. Opik (Comet) debug your AI agent like a boss

What it is: Observability layer for MCP interactions.

Why it rocks:
You can:

• Log every request/response
• Understand tool call patterns
• Debug AI behavior in real time

Use case:

“Why did Claude ignore this config file? What tool did it call instead?”

https://github.com/comet-ml/opik

5. FastAPI-MCP wrap your existing API in minutes

Already have a FastAPI app? You’re 90% done.

What it does:

• Exposes your endpoints to MCP
• Adds tool metadata + discovery
• Plays nice with Claude instantly

Use case:

Your in-house CRM API becomes callable via AI.

https://github.com/danielgrossman/fastapi-mcp

Connecting the dots

Want Claude (or another AI client) to use any of these?

You just:

1. Point the AI to your server via MCP config
2. Define the tools + resources it can access
3. Let it do the rest the LLM handles when/how to call

Next up: how not to shoot yourself in the foot with best practices, common pitfalls, and how to market your server like a dev pro.

Best practices and common traps in the MCP wild

Okay, so you’re building your first MCP tool.
Here’s how to not mess it up and how to make sure people actually use it.

1. Secure it like an adult (not a weekend hacker)

MCP tools can trigger actions. That’s power.
With great power comes… really dumb security mistakes if you’re not careful.

DO:

• Validate all inputs (xss_sanitize(), type checks, etc.)
• Use API keys or token-based auth if your MCP tool isn’t meant to be public
• Limit LLM input to only the routes or actions you expose

DON’T:

• Let a prompt like “delete everything” call a delete_all_users() tool
• Assume AI will never get creative with malformed input

2. Build Claude-first, but don’t Claude-lock

Right now, Anthropic’s Claude is the only LLM with official MCP support.
But that won’t be true for long.

Tip: Design your MCP logic modularly

Make it easy to swap the client:

• Claude today
• Gemini, ChatGPT, or open-source tomorrow

This is the future equivalent of “mobile-first” dev for LLM agents.

3. Market your server like a dev not a marketer

Your MCP server isn’t going viral on its own.
Here’s how to get devs (and AIs 👀) using it:

• Add it to a GitHub repo like: awesome-mcp-tools
• Share it on X/Twitter with #mcp and #buildinpublic
• Write a short post on IndieHackers: “I built a Claude tool that scrapes Hacker News headlines. AMA.”
• Record a 1-minute Loom demo and drop it on Product Hunt
• Post in Discord servers for Claude/LLM/hacker devs

Bonus: Add a “Deploy to Cloudflare” button and link to Stripe if you want to get paid.

4. Start tiny. Iterate fast.

Most of the best MCP tools are built in a day or two.

Weekend roadmap:

• Friday: Identify a painkiller (e.g. “clean CSVs with AI”)
• Saturday: Build a FastMCP or Cloudflare worker + expose 1 tool
• Sunday: Test with Claude + set up Stripe
• Monday: Post it online

This isn’t theory. People are already making passive $$ with tools like:

• extract_links
• analyze_support_tickets
• price_alerts

5. Use AI to build for AI

Yes, seriously. Tools like:

• Cursor or Continue (for AI pair programming)
• Codeium, Phind, or Claude itself (for writing code + docs)

…make building an MCP server shockingly fast. You’re literally using AI to help build the interface for AI. That’s meta and smart.

The future of AI isn’t chat it’s action

Here’s the big idea, in one line:

MCP is how AI agents move from passive brains to powerful doers.

And you the developer are in the perfect spot to build the interface they use to operate in the real world.

Whether it’s:

• Cleaning up CSVs,
• Talking to 200+ data sources via MindsDB,
• Scraping product data,
• Syncing with tools like Notion, Jira, or GitHub…

You can now ship it as an MCP tool, in hours not weeks.

TL;DR recap

• MCP = The “API layer” for LLMs like Claude
• You define tools, resources, and prompts
• Build with FastMCP (Python) or Cloudflare Workers (TypeScript)
• Monetize with Stripe, scale with Cloudflare, and test locally
• Learn from open-source giants like MindsDB, Stagehand, Opik
• Market it like a dev: GitHub, Twitter, Product Hunt
• Start stupid small. Ship fast. Iterate. Profit.

Helpful resources

Here’s your dev starter pack:

• Cloudflare MCP Starter Template: https://github.com/cloudflare/ai
• MindsDB (AI database MCP server): https://github.com/mindsdb/mindsdb
• Stagehand (Browser-based tools): https://github.com/browserbase/stagehand
• Anthropic MCP Docs (Claude): https://docs.anthropic.com/claude/docs/mcp

So…ready to ship your first tool? If you do one, please post in the comments and share how you did and what?

Remember, sharing is caring. 🫶 Thank you in advance!

Claude (and the future of AI agents) is waiting to use it.
Don’t just watch the next AI wave be the one building the surfboards.