Modern AI systems can now perform complex tasks by interacting with external tools, databases, and services, giving rise to AI agents. A significant challenge arises because each application and API exposes its capabilities uniquely, necessitating custom integrations for every AI platform. The Model Context Protocol (MCP) addresses this by providing a standard way for AI models to discover, understand, and use external resources. MCP acts as a common language, allowing AI clients to discover tools, understand their functions, receive structured input, execute them, and get structured results. This protocol is crucial for AI agents that need to perform actions beyond text generation, such as interacting with GitHub, Slack, or databases. MCP involves an MCP client (the AI application), an MCP server (exposing capabilities), and the tools themselves. The process entails the AI client connecting to a server, discovering available tools, invoking a selected tool based on the user's request, and receiving a structured result. For developers, MCP offers standardized integrations, better maintainability, and improved discoverability of tools. Unlike traditional APIs that expose raw endpoints, MCP describes capabilities understandable by AI models at a higher level. While function calling allows AI to invoke predefined functions within an application, MCP provides a broader ecosystem for sharing capabilities across different AI clients. Security is paramount, with MCP servers requiring robust authentication, authorization, and validation. MCP is suitable for various use cases, including coding assistants, enterprise search, and DevOps workflows, and it complements existing APIs rather than replacing them.