MCP — an Architectural Inflection Point
Last Updated on May 2, 2025 by Editorial Team
Author(s): Zoheb Abai
Originally published on Towards AI.
In today’s AI-driven development landscape, applications increasingly rely on sophisticated language models to power intelligent features. However, connecting these models to real-world external sources or services (tools), data sources, and APIs presents significant challenges. The Model Context Protocol (MCP) has emerged as a solution to these integration challenges, providing a standardized approach for AI systems to interact with external data sources and services.
This article follows the development journey of a finance stock analysis application from its simplest form to an AI-powered financial platform. We’ll track how the architecture evolves at each stage to meet growing requirements — from basic stock price lookups to natural language financial analysis that requires AI models and financial data to work seamlessly together.
Through this evolution, we’ll understand when and why MCP becomes not just helpful, but a critical architectural choice for AI-integrated applications.
The simplest application architecture involves a frontend that directly accesses external data sources. Consider this minimal React application that fetches stock data when a user clicks a button.
Even at this stage, the application already uses several standard web protocols and technologies:
HTTP/HTTPS: For all communication between browser and serversWebSockets: For potential real-time updates of stock prices
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