Model Context Protocol (MCP) — An Overview Of Everything You Need To Know

Heard about Model Context Protocol (MCP) but not sure what it actually does? You're not alone — and yet, it's one of the most important shifts happening in AI right now.

Whether you're an engineer, a product manager, or CTO, this article is for you.

This guide will give you a crystal-clear understanding of MCP, including:

What MCP is (and what problem it solves)
How it works under the hood
Why it's suddenly so important
Why you should care
And what it means for the future of AI development

So, let's dive in.

What Is MCP?

Ever been on a trip where you needed to charge your phone, laptop, and camera — all with different plugs?

Then you've probably reached for one of these:

A single universal adapter that connects everything.

That's exactly what Model Context Protocol (MCP) is for AI systems.

Just like a travel adapter connects different devices to a power source, MCP provides a standardized way for AI models to connect with external tools, data, memory, and context — all beyond what was baked into them.

What Problem Does It Solve?

To understand why MCP is a step forward, let's first rewind and look at how things worked before it came along.

Here are some traditional ways developers gave AI systems access to external context or tools:

Custom API Integrations: Where you write custom code for every external service you want the AI to access

For example, if you want your agent to access both the web and your calendar?
You’d have to separately integrate the APIs like Tavily and Google Calendar, each with its own quirks, authentication, and data formats.
A royal pain in the neck!
Plugins (e.g. ChatGPT Plugins): OpenAI introduced plugins as a way to give LLMs controlled access to external APIs using a standardized spec.

While conceptually similar to MCP, there are key differences:
- 🔒 Plugins were proprietary and tied to specific platforms (e.g. ChatGPT, Bing Chat)
- 🧭 Limited flexibility: Mostly supported one-shot API calls (stateless)
- 🔧 Manual effort: Each plugin needed to be individually built and hosted
Framework-Based Tool Use (LangChain, Agents): Frameworks like LangChain popularized tool use by letting developers define custom tools the model could call (like search() or calculate()), with descriptions.

But even then:
- 🔌 Each tool required custom backend logic
- 🛠️ Developers had to wire everything together manually

How MCP Changes the Game:

It offers:

A universal interface for tools and data sources
Plug-and-play extensibility
Two-way, real-time interaction
Open, platform-agnostic architecture
Tool discoverability and runtime flexibility
Ecosystem convergence
A path toward general-purpose AI agents

Why The Hype?

Literally everyone is talking about MCP.

While Anthropic first announced the Model Context Protocol in late 2024, it didn't fully catch on until early 2025.

So why now?

It's built for AI

It isn't another OpenAPI clone. It was designed from the ground up for AI-native systems, with clear separation of
- tools (actions)
- resources (context)
- prompts (intent)
It shipped with the whole stack

Anthropic released:
- Claude Desktop as the first MCP client
- Dozens of reference servers (e.g. Git, Slack, File System)
- Tools like MCP Inspector for testing
- Clean SDKs in Python, TypeScript, and more
And they've been dogfooding it aggressively.
The ecosystem took off

The spec is model-agnostic and community-driven. And it's catching on: over 1,000 MCP servers are live, and major players like Replit, Cursor, and even OpenAI are jumping in.

MCP might not have won yet, but it's leading the race to become the standard for how AI agents plug into the real world.

How Does It Work?

At its core, Model Context Protocol (MCP) creates a bridge between your AI applications and your data, using a simple but powerful client-server architecture.

Here's the breakdown:

Clients (The AI Frontend)

Think of tools like:
- Claude Desktop
- AI-powered IDEs (e.g. Cursor AI)
- Custom assistants embedded in your software
They don't store or manage tools directly, instead, they connect to MCP servers to access external resources and capabilities.
Servers (The Capability Layer)

These are lightweight services that expose data or actions through the MCP protocol.

A server might:
- Connect to local files, databases, or APIs
- Serve up tools like “search this drive” or “query this spreadsheet”
- Return structured responses the AI can use in context
These can run anywhere — locally, in the cloud, or embedded in other systems — and new ones can be added without modifying the AI client.
Primitives (The Building Blocks)

MCP defines three standard components that servers can expose:
1. Resources
  - Inject structured data or documents into the AI's context (like customer profiles, PDFs, or database rows)
2. Prompts
  - Templated instructions to guide how the AI uses a tool or interprets a resource
3. Tools
  - Executable functions that the AI can invoke, like summarize(), search(), or send_email()
These primitives let MCP describe both what the AI should know and what it can do — in a structured, reusable way.

MCP uses JSON-RPC 2.0 to handle communication between clients and servers — a lightweight, widely supported protocol for making structured API calls.

MCP Is Winning — But There Are Still Gaps

MCP in many ways, delivers on its promise.

But let's be clear: it's not a one-size-fits-all solution.

Like any new protocol or ecosystem, MCP introduces its own challenges — technical, architectural, and human.

Here are some key limitations and trade-offs you should know:

Operational Overhead

MCP simplifies integration, but managing multiple MCP servers which in a real-world deployment can get messy.
- You'll need to run and maintain these lightweight services (often locally or in the cloud)
- In production, that means monitoring, logging, uptime guarantees, and security measures
- MCP was initially designed with desktop use in mind — translating that to distributed, multi-user architectures is still a work in progress
Modular doesn't mean maintenance-free.
AI Doesn't Always Use Tools Wisely

Giving the model access to tools doesn't mean it knows when (or how) to use them.
- A model might misuse a tool, skip a step, or take inefficient paths
- Some agents execute redundant or slow operations (e.g. calling 5 MCP servers in sequence instead of parallel)
- Others fail to invoke tools when they should — due to misunderstanding tool descriptions or reasoning errors
Agent-based systems have shown that tool misfires are common, especially when tool descriptions are unclear.
No Built-In Transactionality

Let's say an AI tries to:
- Create a Jira ticket ✅
- Then post a Slack message ❌ (but this one fails)
Now you've got a half-done workflow with no built-in rollback. MCP doesn't provide atomic transactions or error recovery. You either handle it at the agent level or deal with side effects manually.
The Ecosystem Is Young (But Growing Fast)

Even with all the excitement around MCP, it's still early days.
- Support is currently centered around early adopters like Claude, Cursor, and select tooling platforms.
- Using MCP outside that bubble (e.g., with ChatGPT, local models, or custom stacks) often requires adapters or glue code.
- Many tools don't yet expose MCP-compatible servers.
- The spec itself is still evolving. Expect version bumps, new conventions, and sparse docs in some places.
- If you're building something production-grade or long-lived, be ready to refactor as the protocol matures.
Like the early days of USB — the spec exists, but not every device has a “driver.”

Almost all the major providers now have MCP support in their development kits – so it's safe to say that MCP is here to stay.

MCP isn't the one-size-fits-all solution but it's incredibly powerful — especially for teams building AI systems that need rich, dynamic access to tools and data.

Why All Of This Matters

Because MCP follows a modular design, you don't have to touch the AI application itself to add new capabilities.

Want your assistant to talk to a new CRM or vector DB?
Just run or connect an MCP server for it — no need to rebuild or redeploy the agent.

It eliminates data silos

Most companies don't just have one database or knowledge source — they have dozens. CRMs, internal wikis, cloud storage, analytics tools… all siloed.

You can drop in an MCP server for each data type and expose it through a common interface.
It allows for Security and Control Without Compromise

Your data stays in your hands. Servers control what's exposed — no need to share API keys or send sensitive info to third parties.
It avoids vendor lock-in

MCP is open and portable. It works across vendors. That flexibility makes it future-proof.

Recent updates include:

OAuth 2.1 support for more secure authentication with remote servers
Streamable HTTP transport and JSON-RPC batching for improved performance
Tool annotations that provide richer context (e.g. read-only vs. destructive actions)

That means as your needs grow, the protocol grows with you.

Ready To Build With MCP?

MCP is more than just a protocol — it's the foundation for how the next wave of AI systems will connect to tools, data, and real-world context.

If you're looking at eliminating your organization's data silos and creating a seamless AI experience for you and your users, I work hands-on with teams to help them:

Implement MCP in real apps and internal tools
Standardize tool access across messy systems
Build agents that actually use their capabilities effectively

If you're serious about building AI that's production-ready, Let's talk..