An open standard for connecting AI models to external data sources and tools with semantic context.
The Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models connect to external data sources, tools, and services. MCP provides a standardized interface for AI agents to discover available tools, understand what data sources exist, and interact with them in a semantically consistent way. It is analogous to USB for AI — a universal connector that allows any AI model to plug into any data source or tool.
MCP has rapidly become the de facto standard for agentic AI integration in 2026. As organizations deploy AI agents that need to access databases, APIs, file systems, and business applications, MCP provides the semantic glue that makes these connections reliable and consistent. The protocol's semantic layer capabilities — allowing tools to describe their capabilities in machine-understandable terms — are critical for autonomous AI operation.
MCP defines a client-server architecture where AI models act as clients and data sources or tools act as servers. Each MCP server exposes a set of 'resources' (data) and 'tools' (actions) with semantic descriptions. The AI model can query available tools, understand their parameters and expected outputs, and invoke them in a standardized way. This semantic self-description is what allows AI agents to autonomously discover and use new capabilities.
A company builds an MCP server for their CRM system. The server exposes tools like 'get_customer_by_id,' 'list_open_opportunities,' and 'update_deal_stage,' each with semantic descriptions of their parameters and return values. Any MCP-compatible AI agent can now autonomously interact with the CRM without custom integration code.