As the AI landscape rapidly evolves, the demand for systems that support modular, context-aware, and efficient orchestration of models has grown. Enter the Model Context Protocol (MCP) — a rising standard that enables dynamic, multi-agent AI systems to exchange context, manage state, and chain model invocations intelligently.
In this article, we’ll explore what MCP is, why it matters, and how it’s becoming a key component in the infrastructure stack for advanced AI applications. We’ll also walk through a conceptual example of building an MCP-compatible server.
What is the Model Context Protocol (MCP)?
MCP is a protocol designed to manage the contextual state of AI models across requests in multi-agent, multi-model environments. It’s part of a broader effort to make LLMs (Large Language Models) more stateful, collaborative, and task-aware.
At its core, MCP provides:
- A way to pass and maintain context (like conversation history, task progress, or shared knowledge) across AI agents or model calls.
- A standardized protocol to support chained inference, where multiple models collaborate on subtasks.
- Support for stateful computation, which is critical in complex reasoning or long-running workflows.
Source: Internet