MCP (Model Context Protocol) is a new way to give AI access to the right information — securely and automatically — without you having to paste it every time.

It’s popular now because it helps AI feel smarter and more personalized, finally making it useful in real workflows like research, coding, writing, and business tools.

Imagine you’re talking to a super-smart assistant who forgets everything after every conversation. To get useful answers, you need to remind it of who you are, what you’re working on, your goals, documents, preferences — everything, every time.

That’s how most AI models work today. They only see what’s in the current prompt.

Now enter MCP: Model Context Protocol.

MCP is like giving the AI access to a shared memory — a structured, secure way to pull in relevant info about you or your task from other tools, apps, or systems. It lets the AI fetch the right documents, past conversations, settings, or context automatically, without you pasting it in every time.

Think of it as giving the AI the ability to “look around” before answering you: your calendar, your docs, your past chats — whatever you allow.

It’s built in a way that’s modular, private, and secure

Right now, MCP (Model Context Protocol) is mostly used inside Anthropic’s Claude AI system, but it’s designed to be open and usable by others too.

In short: MCP is used wherever you want AI to “remember” or “understand” your world without constantly repeating yourself. It’s still early — but it’s a big step toward AI that actually fits into your life and work.

1. Inside Claude (Anthropic’s AI)

MCP lets Claude connect with external tools and sources — like:

• Your files (Google Drive, Notion, Dropbox, etc.)

• Your calendars, messages, or tasks

• Or even custom apps and databases your company uses

This means Claude can pull in relevant context from those sources automatically to answer questions or complete tasks more effectively.

2. By Developers & Companies

Anthropic made MCP open-source and flexible, so other companies and developers can:

• Build plugins and extensions for AI models

• Create custom memory systems for AI assistants

• Integrate AI into workflows where context really matters (like customer support, legal research, sales, etc.)

3. In Business & Enterprise AI Tools

Companies using Claude or building AI assistants can implement MCP to:

• Personalize responses for users

• Make AI useful across departments (pulling context from CRMs, knowledge bases, etc.)

• Respect security and privacy, while still making AI more powerful

MCP can be a big deal because it gives AI the ability to understand context like a human assistant — pulling in your documents, goals, history, and tools to deliver smarter, faster answers.

Why is that powerful?

Because with the right context, AI doesn’t just answer questions — it can actually do the work.

That means:

• Writing reports with your data

• Researching based on your company’s needs

• Solving tasks across tools like Notion, Slack, Drive

• Making decisions like someone who knows your whole workflow

In short: MCP turns AI from a chatbot into a true digital teammate.

And when that teammate has access to everything you’ve ever learned or built?
It can shortcut 10 years of experience or education — not because it replaces your brain, but because it gives you a second one.

Now let’s talk about swarms of AI Agents. Imagine you had a big group of helpers — like a swarm of bees 🐝 — all working together to solve a problem.

This “swarm” is how some AI systems are built: lots of little parts (or models/agents) working in parallel to think, analyze, and respond.

But sometimes, this swarm is written in a language (like Python) that’s easy to write but slow to run.

So what do developers do?

They take the logic of this swarm and rewrite it in Rust — a much faster, more efficient programming language.

It’s like taking your bee helpers and turning them into jet-powered drones 🚀.

💥 Why does this make it 50–100x faster?

Rust programing language is designed to:

• Use computer memory more efficiently

• Run tasks faster with fewer delays

• Handle lots of things at once without crashing

So when you move the swarm to Rust, it can do the same job in a tiny fraction of the time.

The combo of MCP + Rust-powered swarms is where things get really exciting. Here’s how they enhance each other:

🧠 MCP gives AI the right information

Think of MCP as the AI’s memory and access pass — it connects the AI to the files, calendars, messages, databases, and tools it needs to do useful work. Without this, the AI is just guessing from scratch.

⚙️ Rust-powered swarms give AI super speed and parallel thinking

Now imagine multiple AI agents (a “swarm”) working together, each handling a different part of a task — one finds the data, one summarizes it, one makes a recommendation, etc.
When this is rewritten in Rust, the whole system runs up to 100x faster and more reliably.

🔄 How they enhance each other

1. MCP feeds the swarm: The AI agents get fast, automatic access to everything they need (via MCP), instead of waiting for a human to copy-paste.

2. Rust makes the swarm real-time: With Rust, the swarm can process that context and take actions quickly — almost like a real team of experts working in parallel.

3. Smarter automation becomes possible: You can automate complex workflows (e.g. drafting a report from live business data) instantly, not in minutes or hours.

4. Scale without breaking: Fast + modular = you can serve millions of users with powerful AI agents without your servers melting down.

🚀 In short:

MCP = gives AI brains + memory
Rust-powered swarms = gives AI speed + teamwork
Together, they turn AI into a real-time, context-aware digital workforce.

This is what could replace years of manual work or expertise with intelligent, instant support.

Sources:

https://www.anthropic.com/news/model-context-protocol

https://github.com/The-Swarm-Corporation/swarms-rs