Model Context Protocol (MCP) is a standardized, modular interface designed to orchestrate the interactions between LLM, AI agents, tools, memory, and external systems.

Yet even the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale.

Before Anthropic introduced MCP, AI systems and large language models required bespoke connections to external systems to access information. 

Now, developers can use MCP so they no longer need to hard-code each integration. Agents can dynamically discover, access, and use tools with minimal friction, enabling a more flexible, scalable, and interoperable agentic ecosystem.

Why is model context protocol (MCP) important?

MCP acts as the central control system that coordinates decision-making, context handling, and tool execution — essentially, it’s the “brain” managing the AI agent’s runtime behavior.

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