NHI 101: What is Model Context Protocol (MCP)?

Understanding Anthropic's Model Context Protocol

In the evolving landscape of AI, Anthropic's Model Context Protocol (MCP) offers a revolutionary approach to enhancing how AI models manage and process information. At its core, MCP is designed to improve the ability of AI systems to maintain context across long, complex interactions, ensuring that the system can "remember" key details of a conversation or task over time. This is particularly valuable when dealing with intricate, multi-step processes where retaining information is crucial for providing accurate responses and executing actions.

The MCP is part of a broader effort to make AI models more reliable and intuitive by improving their understanding of context. It allows AI systems to process and respond to information in ways that better align with human expectations. By using MCP, AI agents can dynamically adjust their responses based on previous interactions, ensuring that the model doesn't simply treat each input as isolated but instead factors in the accumulated context of the conversation or task.

A key aspect of this approach is how AI agents manage their Non-Human Identities (NHIs). Much like APIs or service accounts, NHIs represent the automated identities that AI systems use to interact with external systems and services. These identities often need to be secure, persistent, and capable of maintaining context. For example, an AI agent interacting with a cloud service might use an API key as its NHI to securely request data. By utilizing the MCP, the AI can handle such requests with greater efficiency and security, ensuring that the context of prior interactions is preserved across sessions, which is essential when dealing with complex APIs that require multiple steps to complete tasks.

In short, Anthropic’s Model Context Protocol is an innovative tool that allows AI models to handle complex interactions more effectively. By enabling better context management and incorporating NHIs like APIs, AI agents can become more reliable and responsive, driving advancements in automation and AI-driven services.