What the Heck is Model Context Protocol?

So I was vibe-coding my way through an LLM app when I hit a wall. You know the kind — you prompt something like “Build a Next.js app that fetches data from Supabase” and the model just… forgets everything 2 prompts later. 😩

Enter: Model Context Protocol — or MCP for short.

I stumbled on this repo while doom-scrolling GitHub, and honestly? It blew my mind. MCP is like giving your AI working memory. And not just "remember this name" memory — actual scoped, structured memory for apps, projects, and developer workflows.

So... What Is Model Context Protocol?

At a high level:

• Model Context Protocol is an open standard for managing memory, tools, and documents in AI systems. Think of it as a context API, but for LLM agents.

If you're building anything with large language models (ChatGPT, Claude, Gemini), you’ve probably hit the limits of prompt engineering. You want your model to “know” about:

• your file structure
• open tabs
• your docs
• a running task list
• API credentials

MCP says: “Cool, let’s formalize that.😂”
It introduces ideas like:

• Workspaces (like projects)
• Documents (files, notes, tasks)
• Tools (executables, like dev servers or linters)
• Models(your LLMs)

And it lets you wire them together like LEGO.

Why Does This Matter?

Because the future of dev tools is LLM-native.
Right now, we’re hacking autocomplete and chat into VS Code. But what we really want is an AI partner that:

• understands project structure
• knows what's in scope
• remembers what you asked earlier
• can run real tools and reflect on the output

MCP gives us the plumbing for that.

How Does It Work?

The servers repo gives you reference implementations of an MCP server — it handles state management for your AI agent.

You can:

• Create a Workspace via API
• Add files, tools, notes
• Start a session with your favorite LLM (OpenAI, Anthropic, etc.)
• Send prompts that pull from structured context

Instead of sending giant prompts every time, you give your model a memory, a brain, and some hands.

What’s Cool for Beginners?

I’m still wrapping my head around agentic coding, but MCP is beginner-friendly if:

• You're building an LLM app and sick of prompt bloat
• You want structured memory without wiring up Redis, LangGraph, etc.
• You like open standards (this is all public + MIT licensed)

Here’s how you could start:

• Clone modelcontextprotocol/servers
• Run the server locally with Docker
• Hit the API with curl or Postman to create your first Workspace
• Hook it up to an LLM (OpenAI key, Anthropic, etc.)
• Start querying with scoped memory!

Bonus: You can use MCP client libraries in Python, TypeScript, and Rust.

Why I’m Hyped?

This feels like the Rails moment for agentic systems. Not just "cool demos", but a standardized way to build dev tools that remember.

It’s not just about one repo. It's about the idea that your LLM shouldn't start from scratch every prompt.

And that’s the vibe. 💡

Resources

• 🔗 Model Context Protocol GitHub
• 📚 MCP Spec
• ⚙️ Servers Repo
• 🧠 Want to build an LLM IDE? Start here.

TLDR

If you’re building with LLMs in 2025, Model Context Protocol is your new best friend. It gives your model memory, tools, structure — and a better chance at shipping real software, not just vibe-code spaghetti.

Follow me for more breakdowns like this. ✌️

Comments

[Arindam Majumder]

Great Share, Ankit!

I also made a video around this!

[Ankit Kumar]

Thanks for mentioning this Video, Arindam!

[Nevo David]

this is super clutch tbh - i’m always hunting for ways to make things stick across sessions. you ever worry that too much structure could kill some of the ‘aha’ moments we get from messy workflows?

[Ankit Kumar]

Totally feel you - that messy, chaotic space is often where the best ideas happen. I think the goal of MCP isn’t to eliminate that, but to support it by holding the context you choose to persist. The structure’s there when you need it, but it doesn’t force a rigid workflow.

Think of it like giving your LLM a notebook, not a rulebook. 😉