🔌 MCP Server Explained: The Open Protocol That Gives AI Superpowers (And Why Indian Developers Should Care!)

🧠 What if AI could not only answer your questions, but also Google stuff, write code, push to GitHub, query your database, and fetch latest docs from the web — all by itself?

Sounds futuristic? With MCP (Model Context Protocol) — it’s not.

Let’s dive into the world of MCP and understand why it’s a game-changer for developers, techies, and AI builders — especially here in India, where the developer ecosystem is exploding 🚀.

🤔 What is MCP?

Model Context Protocol (MCP) is an open standard introduced by Anthropic that makes it super easy to connect LLMs (Large Language Models) like GPT, Claude, or DeepSeek with external tools, APIs, databases, apps, and even your own files.

You can think of MCP as the “USB-C” of AI — a universal connector between the AI brain and everything else it needs to talk to.

Whether you’re building an AI agent, coding assistant, smart IDE plugin, or an automated research assistant — MCP can be the backbone of that system.

🤯 The Problem MCP Solves

Let’s say you’re building a coding assistant powered by an LLM. The assistant needs to:

• Read code from your local files 📁
• Search Stack Overflow or documentation 🌐
• Interact with GitHub (create PRs, review code) 🛠️
• Run SQL queries on your local database 🧮

Before MCP, each integration required a custom setup. That means, if there are N models and M tools, you’d end up building N × M connectors. 💸 Painful and inefficient.

With MCP, you write the integration once. It works with any MCP-compatible model or client. 💡

🧱 MCP Architecture – Simple Yet Powerful

MCP follows a client-server model with three parts:

1. Host

The front-end app where the user interacts with the AI — like your IDE (Cursor, VS Code), AI assistant, or chat interface.

2. Client

This lives inside the Host. It connects the LLM to an MCP server — like a bridge between your app and external tools.

3. Server

This is where the magic happens ✨. The MCP server is a lightweight program (in Python or TypeScript) that exposes tools, data, or instructions. It talks to APIs, fetches files, runs DB queries — basically does whatever your AI needs.

🛠️ Core Building Blocks (Primitives) of MCP

MCP defines five main components (aka primitives) that power its standardised communication:

1. 🛠️ Tools

Think of tools as functions the LLM can call. You define the function name, input/output schema, and what it does.

Examples:

• search_google(query: string)
• get_user_info_from_db(user_id: int)
• create_github_issue(title, body)

➡️ This is the most used primitive. Tools let AI do things outside itself.

2. 📦 Resources

These are data objects or files the AI can read to get more context.

Examples:

• Code from a local .py file
• Records from a PostgreSQL table
• Output from a REST API

They help the LLM make better decisions because it has real-time, external context.

3. ✍️ Prompts

Reusable instruction templates. They guide how the LLM should approach a task.

Examples:

• “Write a detailed code review in markdown.”
• “Summarise this research paper in bullet points.”

Prompts give structure and consistency to AI outputs.

4. 🌳 Roots (Client-side)

This creates a secure file access pathway from your local machine.

Example: LLM wants to open a file — Roots ensures it happens safely, without giving blanket access to the entire system.

Useful for dev tools and IDE integrations.

5. 🧪 Sampling (Client-side)

Let the server ask the LLM for help. Yes, it works both ways!

Example: The server is building a SQL query but isn’t sure how to structure it. It asks the LLM:

“Given this schema, what’s the best way to query top users?”

This two-way collaboration makes AI + tools smarter together.

🧰 How MCP Servers Are Built

Building an MCP server is surprisingly simple! 🧑‍💻

You can use:

• 🐍 Python SDK
• 🟦 TypeScript SDK

You define:

• Tools (with schema using Zod or Pydantic)
• Handlers for what to do when a tool is called
• Resources to expose
• Prompts for task guidance

⚙️ MCP handles the communication and standardisation. You focus on writing useful tools.

🌐 How MCP Servers Talk to AI Clients

There are two transport layers (aka communication styles):

1. 🖥️ STDIO (Standard Input/Output)

Ideal for local use — server reads/writes from terminal streams. Works great for CLI tools or Docker containers.

🧾 Example:

python my_mcp_server.py

2. 🌍 SSE (Server-Sent Events)

Great for remote servers — works via HTTP POST and streaming.

You set up an Express.js server or similar backend. The AI client connects via a URL.

🧾 Example config:

{
  "url": "https://my-server.com/mcp",
  "transport": "sse"
}

🧠 Real-World Use Cases for MCP in India

MCP can revolutionise the way Indian devs build and interact with AI systems:

🧑‍💻 AI Coding Assistants

Integrate tools like:

• Fetch current project files
• Edit code from prompt
• Generate tests
• Push changes to GitHub

➡️ IDEs like Cursor, VS Code, and Windsurf use this pattern.

🔎 External Knowledge Access

Build servers that crawl:

• Indian Government data portals
• IRCTC APIs
• LIC policy documentation
• IRDA, SEBI websites
  … and inject the content as resources for LLM to use!

📊 Database + DevOps Integration

Expose tools like:

• create_table
• run_query
• get_failed_builds_from_CircleCI

Imagine managing your DB or CI pipeline from plain English commands. 🎯

🌐 Web Search + RAG

Combine web scraping + RAG to fetch content from:

• Tech forums
• Indian dev blogs
• Company APIs

Use it for informed content generation, summarisation, or research assistants.

🧑‍🎨 Software Control

Imagine this:

“Render a YouTube intro in Adobe Premiere with background music.”

If Premiere is MCP-integrated, LLM can do it.
You could even create tools for:

• Canva 🖼️
• Figma 🎨
• Blender 🧊

Design + AI = 🔥 new workflow.

💰 Earning Potential with MCP

1. Hosted Servers

Host MCP servers that perform complex tasks and charge users via subscription. Examples:

• Advanced video editing
• Localisation and translation tools
• Report generation for Indian startups

2. Integration as a Service

Companies are looking to connect their tools with AI.

You can offer:

• MCP plugin for Tally or Zoho
• MCP integration for BharatPe, Razorpay, or UPI data

3. MCP Tool Marketplace (Future Vision)

Think of an app store where devs upload their MCP servers/tools — and others can subscribe or install.

Build once, sell many times.

🇮🇳 Why Indian Developers Should Jump In NOW

India is one of the fastest-growing tech ecosystems. With more devs moving to AI and automation:

• MCP gives superpowers to your apps
• It’s open-source and community-friendly
• You can be early in building tools that serve a massive market

Whether you’re a solo dev, startup founder, or enterprise builder — MCP is your gateway to next-gen AI integration. 🔑

🛠️ Getting Started

1. Visit the official MCP GitHub repo
2. Choose your stack: Python or TypeScript
3. Pick a use case (tool, resource, or prompt)
4. Build your first MCP server
5. Connect it to Cursor or any MCP-compatible IDE
6. Watch AI and tools work together 💥

🔚 Final Thoughts

MCP isn’t just a protocol. It’s a paradigm shift.

Instead of keeping AI trapped inside chat boxes, it sets it free — to code, build, browse, automate, and create.

India’s tech brains have always led from the front in the open-source and dev tools revolution. Let’s take the lead in AI-native tooling, too.

AI + MCP = Developer Nirvana 🙏
Don’t just build apps — build AI-powered ecosystems.

Liked this deep-dive? Share it with your fellow developers. Or better — build something cool with MCP and let the world see what Indian tech can do! 🇮🇳🚀