Model Context Protocol: The Plumbing That Makes AI Actually Useful

There's a particular species of technical tutorial that explains how to build something without ever clarifying why you'd want to. This isn't one of those—at least not after we translate what developer Asif Razzaq actually built and why it matters for anyone trying to deploy AI that does more than generate text.

According to Razzaq's tutorial published October 19, 2025, the Model Context Protocol (MCP) solves a fundamental problem: AI models are trained on static datasets but need to interact with live information and external tools to be genuinely useful. MCP creates the infrastructure for models to access real-time resources, execute specialized tools, and maintain context across interactions.

Think of it as building the plumbing that connects your AI to everything else that matters—databases, APIs, search engines, analytics tools, customer records. Without this plumbing, you're stuck with an AI that only knows what it learned during training and can't act on anything current.

What MCP Actually Does

The protocol defines three core components that work together: resources (external data sources), tools (functions the AI can execute), and messages (context from ongoing interactions).

In Razzaq's implementation, the MCP server manages available resources and tools while handling execution requests. The MCP client connects to servers, queries what's available, fetches data, and executes tools while maintaining conversational context. This architecture enables AI systems to move beyond generating responses based solely on training data to actually retrieving current information and performing operations.

The tutorial demonstrates this with practical examples: sentiment analysis on text, summarization with configurable length limits, and knowledge base search with ranked results. These aren't hypothetical capabilities—they're the building blocks for AI applications that need to analyze customer feedback, process documents, or search company knowledge bases in real time.

Why This Architecture Matters

Traditional AI deployments hit a wall when they need current information. A model trained through January 2025 can't tell you what happened in October 2025 unless someone builds infrastructure for it to access that data. MCP standardizes how that infrastructure works.

The asynchronous design Razzaq implements is critical for production applications. When an AI needs to fetch customer records from a database, search a knowledge base, and analyze sentiment on recent reviews, those operations happen concurrently rather than sequentially. This matters at scale—the difference between responses that take two seconds versus ten.

The context window management enables stateful interactions. The system tracks what resources were accessed, which tools were executed, and what information was retrieved. This contextual memory allows subsequent requests to build on previous interactions instead of starting fresh each time.

The Technical Decisions That Create Value

Razzaq's implementation uses dataclasses for clean structure representation, making the system easier to extend and maintain. Resources carry metadata (URI, name, description, MIME type) alongside their actual content, enabling intelligent routing and caching decisions.

Tools include parameter specifications that define required inputs, types, and defaults. This structured approach prevents runtime errors and enables automatic validation before execution. The optional handler function allows tools to be registered with or without immediate implementation—useful for prototyping workflows before building full functionality.

The separation between server and client creates deployment flexibility. Servers can run close to data sources with appropriate security controls while clients operate in user-facing applications. Multiple clients can connect to shared servers, enabling consistent tool access across different applications without duplicating logic.

What This Enables for Business Applications

Consider a customer service AI that needs to check order status, review purchase history, analyze sentiment in previous interactions, and search internal documentation for relevant policies. Without MCP-style architecture, each of these capabilities requires custom integration code. With MCP, they're registered tools and resources that any client can access through standardized interfaces.

The protocol makes AI applications modular. Need to add a new data source? Register it as a resource. Need to enable a new capability? Add it as a tool. The core AI logic doesn't change—it just gains access to new functionality through the existing protocol.

This modularity accelerates development and reduces maintenance burden. When your customer data schema changes, you update the resource handler, not the AI application code. When you need to swap sentiment analysis providers, you modify the tool implementation without touching client logic.

The Shift From Static to Dynamic Intelligence

Razzaq frames this as "breaking the boundaries of static AI systems"—treating models as components within larger systems rather than complete solutions. That perspective is correct and underappreciated.

The value of GPT-4 or Claude isn't just their language understanding—it's their ability to serve as reasoning engines within applications that connect them to real-world data and capabilities. MCP-style protocols make building those applications practical.

We're moving from asking "what can this model do?" to "what can we enable this model to do?" That shift requires infrastructure. It requires standardized ways for models to discover available resources, understand tool capabilities, and maintain context across complex workflows.

The Implementation Reality

Razzaq's tutorial walks through building a complete MCP system from scratch in Python, demonstrating resource registration, tool execution, and context management. The code examples show asynchronous handlers for sentiment analysis, text summarization, and knowledge search—operations that represent common requirements across business AI applications.

The demonstration sequence reveals how the pieces work together: listing available resources, fetching sales data, analyzing text sentiment, summarizing content, searching knowledge bases, and reviewing interaction context. These aren't isolated capabilities—they're composable operations that can be orchestrated into complex workflows.

For developers implementing AI features, this architecture provides a proven pattern. For business leaders evaluating AI investments, it clarifies what infrastructure is required beyond just model access.

What This Means Going Forward

Anthropic recently announced MCP as a standard protocol, with multiple companies building implementations. The fact that developers are publishing detailed tutorials on building MCP servers and clients signals growing adoption beyond just Anthropic's ecosystem.

Standardization matters because it enables interoperability. When multiple AI platforms support the same protocol for tool access and resource integration, applications become portable. You're not locked into vendor-specific integration approaches—you're building on shared standards.

This is similar to what happened with REST APIs or OAuth for authentication. Initial implementations were custom and fragmented. Standards emerged. Eventually, standardized approaches became expected rather than exceptional.

We're likely in the early standardization phase for AI-to-tool integration. MCP is one proposed standard. Others will emerge. The implementations that gain traction will shape how AI applications are built for the next decade.

MCP Infrastructure and the Future of AI

The Model Context Protocol isn't glamorous. It's infrastructure. But infrastructure determines what's possible at the application layer.

Razzaq's tutorial demonstrates that building dynamic AI systems—ones that can access current data and execute operations beyond text generation—requires thoughtful architecture. MCP provides that architecture through standardized server-client patterns, resource management, tool execution, and context tracking.

For organizations deploying AI, the question isn't whether you need this kind of infrastructure. You do, if your AI needs to do anything beyond answering questions from training data. The question is whether you build custom integration logic for every data source and capability, or adopt standardized protocols that make AI applications modular and maintainable.

The difference between those approaches determines how quickly you can ship AI features and how much pain you'll experience maintaining them.

If you're building AI applications and need to design integration architecture that actually scales, our growth strategists understand both the technical requirements and business constraints. Let's talk about building AI systems that work in production, not just demos.