The future of AI applications: MCP servers

In an era defined by rapidly evolving AI capabilities, the demand for highly scalable, connected, and interoperable infrastructure is only intensifying.

Advancements in AI and distributed systems are shaping the architecture that supports global digital ecosystems.

AI integration continues to climb at pace. According to McKinsey in ‘The State of AI’ report, over three-quarters (78%) say their organizations use AI in at least one business function, up from 55% in 2023.

AI is being practically used for workflow automation, and we’re seeing huge pickup on agentic capabilities.

On this, PwC outlines nearly two-thirds (66%) of agentic AI adopters are reporting increased productivity.

Focusing more on the technical side, one of the most exciting and transformative developments in the AI space is the emergence of Model Context Protocol (MCP).

While still gaining mainstream awareness, MCP architecture has quietly become foundational for the next generation of AI and machine learning applications.

The rise of the MCP server

At its core, an MCP server is a flexible, modular computing environment designed to support distributed, AI-powered systems at scale.

It functions as the connective tissue enabling AI workloads, data processing, and intelligent decision-making to occur across a blend of devices and environments, whether cloud, edge, or on-premises.

Where traditional servers are often siloed, static, and inflexible, MCP servers are composable and context-aware. They dynamically allocate resources based on application demands, connect seamlessly to multiple networks and data sources, and adapt to workload variability in real time.

What’s the hype behind MCP?

MCP uses a client-server model that links host applications, like Claude Desktop, IDEs, or enterprise AI platforms, to lightweight servers and data sources.

MCP hosts run LLM-powered apps, MCP clients maintain direct connections to servers inside the host, and MCP servers expose specific capabilities, tapping into local files, databases, or remote APIs and cloud systems.

This setup delivers rich, context-aware AI across diverse environments. Its extensible communication stack includes a protocol layer (for framing messages, pairing requests and responses) and a transport layer (using Stdio for local and HTTP+SSE for remote async communication).

All messaging runs on JSON-RPC 2.0 for lightweight, interoperable data exchange.

Once connected, MCP supports asynchronous request-response tasks, one-way notifications, and clean shutdowns, with robust error handling built in. The result is a fast, resilient architecture ready for real-time, production-grade AI, even in regulated sectors.

The practical side: MCP in the tech toolkit

Teams are embedding MCP into their workflows to supercharge development. By linking MCP servers to internal Git environments, they create a “Git bridge” that gives AI direct context on their codebase, with no retraining or fine-tuning needed.

The AI can instantly generate or refactor code with full awareness of architecture, dependencies, and logic, cutting friction and speeding up iteration cycles.

This ultimately creates a stronger, mutual development relationship between human and machine. Engineers can focus on higher-level problem solving while MCP handles scaffolding, test generation, and even cross-language translation. As platforms scale, this kind of AI-native infrastructure becomes essential for sustaining both velocity and quality.

Unlocking MCP’s potential

Beyond code, MCP is a new architectural primitive for real-time decisioning. It can drive compliance frameworks that adapt as regulations shift, fraud models that evolve with adversaries, and payment routing that reroutes based on live fees, congestion, or jurisdictional rules.

MCP doesn’t just run code, it orchestrates intelligent systems that learn and adapt at the speed of change.

Most businesses are still tethered to centralized clouds. That simplifies provisioning but creates latency, lock-in, and fragility.

Real-time systems choke on long network paths, and innovation stalls under vendor constraints. By decoupling compute from centralized clouds, MCP frees workloads from bottlenecks.

Compute can run anywhere: near data, at the edge, or across markets, all while staying part of a cohesive network.

This slashes latency, boosts reliability, enables elastic scale, and preserves regulatory sovereignty. If a region or provider fails, workloads simply migrate. Instead of brittle stacks, MCP builds antifragile infrastructures that get stronger under stress.

Agility is no longer optional, it’s a condition for survival in the modern business landscape. Decoupling compute makes that agility structural, not aspirational, and MCP turns infrastructure from a bottleneck into a competitive asset, letting companies launch new services, markets, and models instantly.

The road ahead for MCP

MCP is already powering real-world breakthroughs. We’re seeing DeepSpeed accelerating distributed LLM training, TensorFlow Federated enabling decentralized learning, PyTorch on Kubernetes scaling AI workloads on demand, ONNX Runtime optimizing inference across hardware, and digital twins driving real-time automation in smart factories.

As AI, blockchain, and adaptive infrastructure converge, MCP servers are emerging as the digital backbone, delivering the low-latency, high-throughput, context-aware computing that next-gen systems demand.

If you’re building modular AI frameworks, decentralized apps, or cloud-native platforms, MCP could be the cornerstone of your future stack and a powerful foundation for collaboration and innovation.

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