This paper presents a comprehensive analysis of Model Context Protocol (MCP), a revolutionary standardized approach to connecting artificial intelligence models with external resources, tools, and data sources. Unlike traditional agentic systems that rely on complex orchestration layers, MCP provides a direct and efficient pathway for AI-resource interaction through a unified client-server architecture. The study demonstrates practical implementations, including weather forecast applications and enterprise-grade CORTEX agent integration with Claude Desktop. The research validates MCP's four core architectural pillars: Resources, Tools, Server, and Client components, showcasing their effectiveness in real-world scenarios. Key findings indicate that MCP significantly reduces system complexity while enhancing functionality, offering superior integration capabilities compared to conventional agentic approaches. The implementation demonstrates seamless natural language query processing, real-time data access, and multi-source response synthesis, establishing MCP as a foundational protocol for future AI ecosystem development.