The Model Context Protocol, or MCP, is an open standard introduced by Anthropic in late 2024 that defines a common interface for connecting AI language models to external tools, data sources, APIs, and services. Before MCP, every AI integration required custom implementation: a developer building a connection between an AI assistant and a database, CRM, or internal tool had to write bespoke code for each integration. MCP provides a shared protocol that any compliant model and any compliant tool can use to communicate, reducing integration work and improving interoperability.
MCP works through a client-server architecture. An MCP server exposes a set of tools, resources, or prompts. An MCP client, typically an AI application or agent, discovers and calls those tools through the standard protocol. This means an MCP server built for one AI application can be used by any other application that implements the protocol, and a new AI model that supports MCP automatically gains access to the entire ecosystem of existing MCP-compatible tools.
The practical effect is that building AI systems that interact with real-world services becomes significantly faster and more maintainable. Instead of custom integrations for every data source, developers build MCP servers once and make them available across multiple AI applications. The growing ecosystem of MCP connectors covers databases, communication tools, code repositories, CRM systems, and many other enterprise services.
For organizations evaluating AI infrastructure, MCP compatibility is increasingly relevant to platform decisions. AI development tools, orchestration frameworks, and enterprise software vendors are rapidly adding MCP support. Teams that build on MCP-compatible foundations reduce long-term integration complexity and make their AI systems easier to extend as new capabilities become available.