The Model Context Protocol (MCP) is an open standard designed to solve the fragmentation caused by developers needing to build individual integrations for every AI assistant and service. Instead of creating bespoke connections for each platform, developers can implement a single MCP server that acts as a secure bridge between AI clients (such as Claude Desktop, Cursor, or ChatGPT) and external data sources like GitHub, Slack, PostgreSQL, or local file systems. The architecture consists of three primary components: the AI Client, which manages requests and displays responses; the MCP Server, which executes logic and retrieves data; and the External Services. MCP servers expose three core capabilities: Tools (executable functions like get_weather() or execute_sql() ), Resources (readable data such as documentation or database records), and Prompts (reusable templates for tasks like generating bug reports). By utilizing this protocol, an AI model can interact with a database through a structured workflow—where the client requests a tool, the server performs the operation, and the result is returned in a standardized format—without the model ever accessing sensitive credentials directly. This standardization aims to provide the same interoperability for AI ecosystems that REST provided for web APIs and Docker provided for application packaging.