In the last two years, we’ve witnessed an explosion in AI capabilities. Large language models (LLMs) like Claude, GPT-4, Copilot, and Gemini are powering everything from customer support to code generation. But as these models grow more powerful, a new bottleneck has emerged—not in the models themselves, but in how we connect them to the world.

Every new AI tool seems to reinvent the wheel: custom APIs, brittle integrations, and siloed data. Developers are drowning in glue code. Enterprises are struggling to scale. And users are left wondering why their AI assistant can’t remember what they did yesterday.

This is the moment for the Model Context Protocol (MCP). To understand the basics of this concept (MCP), there is a website that provides the documentation you need to get foundational knowledge on the subject. Click on the link here.

Current Landscape Highlights

There is a website called Smithery which is a major MCP registry and installer site for MCP servers. Currently it lists over 6,500 MCP servers and is facilitating over 1.2 million tool calls per month. It touts over 6600 users have subscribed to its services.

Meanwhile, in the MCP client space is Claude Desktop. It is a leading MCP client, but it’s not alone—there are emerging clients and integrations with platforms like Slack, Discord, GitHub, Google Drive, and Cloudflare 2. But right now, when you ask about MCP clients, the prominent tool reference in response is Claude Desktop.

In GitHub there are reference server repositories that showcases dozens of production-ready and community-built MCP servers, covering use cases from browser automation to database access.

Custom MCP servers are gaining traction, especially for interfacing with proprietary or domain-specific systems.

The Problem with the Current Landscape

AI tools today are like brilliant musicians playing in different keys. Each one is powerful on its own, but together? It’s chaos.

Fragmentation: Every tool speaks a different language.
Reinvention: Developers rebuild the same scaffolding over and over.
Silos: Context is lost between tools, sessions, and systems.

Without a shared protocol, AI systems can’t collaborate. They can’t remember. They can’t evolve.

Enter the Model Context Protocol

The Model Context Protocol is a simple but powerful idea: a standard way for AI models to interact with tools, data, and each other—using shared context. Putting it simple, MCP is a protocol for connecting AI models to tools, data, and each other using a shared context.

Think of it as the HTTP of AI agents. It defines how tools are described, how context is passed, and how models can invoke capabilities dynamically.

MCP isn’t a product. It’s a protocol. That means it’s interoperable, extensible, and open—designed to work across clients, servers, and ecosystems.

A Brief History of MCP

The Model Context Protocol (MCP) was officially introduced by Anthropic in November 2024 as an open standard and open-source framework designed to solve one of the most pressing challenges in AI: how to connect large language models (LLMs) to external tools, systems, and data sources in a scalable, standardized way.

Before MCP, developers faced what Anthropic described as an “M×N integration problem”—every new model (M) had to be manually integrated with every new tool or data source (N), often requiring custom APIs, prompt engineering, and brittle glue code. This approach was not only inefficient but also unsustainable as the number of models and tools exploded.

MCP was created to turn that M×N problem into an N+M solution: tools and models each conform to the MCP standard once, and then any model can work with any tool that follows the protocol. This is similar to how ODBC standardized database connectivity in the 1990s—MCP is often described as the “ODBC for AI.”

Anthropic’s vision was to break AI systems out of isolation by giving them a universal “USB port” to plug into the broader digital ecosystem. MCP defines how tools are described, how context is shared, and how models can invoke capabilities dynamically and securely.

This is my two-cents to the MCP concept. Consider the Borg if you’re a Star Trek fan. They were capable of beaming on board your ship and then penetrating your devices to download useful information that would increase their knowledge and capabilities. I view MCP as Borg-like. We now have a way for non-AI applications to dialog with AI applications with ease.

Since its release, MCP has been adopted by major AI platforms, including Claude Desktop, and supported by tools like Smithery, which acts as a registry and installer for thousands of MCP servers. The protocol has rapidly gained traction in both open-source and enterprise environments, enabling everything from natural language database queries to real-time code editing and business process automation.

Here is a visual diagram that complements this article:

Fragmented AI Tool Landscape (Pre-MCP) Interoperable Ecosystem (with MCP)

Diagram Explanation:

Left Panel – Fragmented AI Tool Landscape (Pre-MCP):
- Each tool is connected to a specific model via custom, one-off integrations.
- This illustrates the inefficiency and lack of interoperability.
Right Panel – Interoperable Ecosystem (With MCP):
- All tools and models connect through a central MCP node.
- This shows how MCP simplifies integration and enables composability.

But Why Now?

It is obvious that the timing couldn’t be better. And why? Here are some reasons:

LLMs (Large Language Models) are everywhere, but integration is the bottleneck.
Thousands of MCP servers already exist, offering tools for browsing, coding, querying, and more.
Smithery makes it easy to install and manage MCP servers on your local computer.
Claude Desktop and other clients are proving how powerful MCP can be in real-world workflows.

Since the release of MCP in late 2024, the Model Context Protocol (MCP) has rapidly evolved from a promising concept into a foundational layer for AI integration. Its momentum is being driven by three powerful forces: open-source innovation, a vibrant developer community, and real-world deployments that are already delivering measurable value.

Open-Source Innovation

MCP was launched as an open standard by Anthropic, and its open-source nature has been key to its rapid adoption. Developers around the world have contributed to:

Reference implementations in Python, Java (Spring AI MCP), and other languages.
Toolkits and SDKs that make it easy to build MCP-compliant servers and clients.
Compliance test suites to ensure interoperability across implementations

This open ecosystem has enabled rapid iteration and experimentation, with new features like streaming, multimodal support, and agent graphs already in development

Community Growth

The MCP community has grown into a global network of developers, researchers, and companies. Highlights include:

Thousands of MCP servers listed on registries like Smithery, covering domains from finance to design
Active GitHub discussions and proposals, where contributors shape the protocol’s evolution.
Community-led governance, ensuring that MCP remains inclusive, transparent, and adaptable

This collaborative spirit has made MCP not just a protocol, but a movement.

Real-World Use Cases

MCP is already powering real-world applications across industries:

Claude Desktop uses MCP to fetch live project data from tools like GitHub and Google Drive, boosting developer productivity by 30%
Spring AI MCP, a Java-based implementation, is used by financial firms to connect LLMs to real-time stock data, improving trading predictions by 15%
Creative tools like Unity and Figma are integrating MCP to enable AI-assisted design workflows

These aren’t just proofs of concept—they’re production systems delivering tangible results.

What Makes MCP Different

In a world overflowing with APIs, SDKs, and integration frameworks, it’s fair to ask: What makes MCP special? The answer lies in its design philosophy, technical architecture, and ecosystem-first approach. MCP isn’t just another tool—it’s a protocol, and that distinction makes all the difference.

1. Protocol-First, Not Product-First

Most AI integrations today are built around products—closed systems with proprietary interfaces. MCP flips that model on its head. It’s protocol-first, meaning:

Anyone can implement it — clients, servers, tools, or agents.
It’s not tied to a single vendor or platform.
It encourages interoperability rather than lock-in.

This is the same principle that made the web flourish: open protocols like HTTP, not closed platforms.

2. Composable by Design

MCP treats tools and resources as modular components that can be:

Discovered via registries like Smithery
Mounted dynamically into model contexts
Chained together to form complex workflows

This composability means developers can build once and reuse everywhere. According to Google, composability is about the ability to combine different components or modules to create a larger, more complex system or solution. It also enables agentic behavior, where models can reason about which tools to use and when.

3. Context-Aware Interactions

Unlike traditional APIs, MCP is built around the idea of shared context. This allows:

Models to maintain memory across tool invocations
Tools to adapt their behavior based on prior interactions
Seamless handoffs between tools, models, and users

This context-awareness is what makes MCP ideal for multi-turn conversations, agent frameworks, and LLM-native applications.

4. Lightweight and Extensible

MCP is intentionally minimal. It defines just enough to ensure compatibility, while leaving room for innovation. Key features include:

Standard transport protocols (HTTP, SSE, STDIO transport)
Simple JSON-based schemas
Extensible metadata for custom capabilities

This makes it easy to implement, test, and evolve—whether you’re building a CLI tool, a cloud service, or a mobile app.

5. Ecosystem-Driven Evolution

MCP is not a static specification. Instead, it’s a living protocol shaped by its community. Its evolution is driven by:

Real-world use cases
Open-source contributions
Transparent governance

This ensures that MCP stays relevant, practical, and aligned with the needs of developers and organizations.

MCP is designed for the world we’re entering—a world of autonomous agents, dynamic workflows, and AI-native applications.

What’s at Stake

The future of AI isn’t just about better models—it’s about better systems. Systems that can reason, act, and adapt across tools, data, and domains. Without a shared protocol like MCP, we risk building a future that’s:

Fragmented: Every tool and model locked into its own ecosystem.
Redundant: Developers waste time rebuilding the same integrations.
Brittle: Systems that break when tools change or scale.

But with MCP, we unlock a different future—one where:

Interoperability is the default: Tools and models can plug into each other like Lego bricks.
Context flows freely: AI systems can remember, adapt, and collaborate.
Innovation accelerates: Developers build on each other’s work instead of starting from scratch.

This isn’t just a technical shift—it’s a strategic one. The organizations that embrace MCP now will be the ones who move faster, integrate deeper, and scale smarter in the AI-native era.

Call to Action

The Model Context Protocol isn’t just a specification. Instead, it’s a signal. A signal that the AI community is ready to move beyond isolated tools and toward cooperative, contextual, and composable systems. (I surely hope this is true.)

If you’re a developer, test analyst, a founder, a researcher, or a product leader, now is the time to:

Explore the ecosystem: Visit registries like Smithery and see what’s already possible.
Try a client: Use Claude Desktop or another MCP-compatible interface to experience the protocol in action.
Build your own: Create a custom MCP server that connects your tools, data, or workflows to the broader AI ecosystem.
View at least my video on MCP: My first video on the subject is entitled “MCP (Model Context Protocol) and MySQL Introduction”. Click here to go directly to the video.

The protocol is ready. The tools are here. The community is growing.

This is the MCP moment. Don’t miss it.

In the next article, we’ll explore the ecosystem that’s already forming around MCP—from Claude and Smithery to the thousands of servers powering the next generation of AI tools.

The MCP Moment – Why Now?

The AI Boom Needs a Backbone