What is the Model Context Protocol (MCP) and how does it benefit AI agents?

The Model Context Protocol (MCP) is an open standard for connecting AI agents to external data sources and tools through a unified interface. Instead of building custom integrations for each tool, MCP provides a standardized protocol that any compliant tool server can expose and any compliant AI client can consume. Architecture: MCP servers expose resources (data to read), tools (actions to perform), and prompts (reusable templates). MCP clients connect to servers and present available capabilities to the LLM. Benefits: tool interoperability (any MCP-compatible agent works with any MCP server), reduced integration effort (build once, use everywhere), dynamic tool discovery (agents discover available tools at runtime), and ecosystem growth (shared tool servers benefit the entire community). Implementation: create an MCP server that exposes your API as tools with JSON Schema parameter definitions, connect to it from Claude, Cursor, or any MCP-compatible client. Server types: stdio (local process communication) and SSE/HTTP (remote server communication). MCP replaces the need for per-tool integrations, similar to how USB standardized peripheral connections. Production considerations: implement authentication on MCP servers, rate limit tool calls, validate parameters, and log all tool invocations for audit.