Author(s): R. Thompson (PhD) Originally published on Towards AI. “Context is not just a variable. It’s the difference between output and understanding.” The evolution of AI agents has moved beyond prediction and instruction-following into a domain where interpretability, confidence, and memory are essential features. With growing adoption of models like Claude, Anthropic’s highly aligned natural language model, the AI community now faces a pressing need to bridge reasoning performance with persistent, structured memory. This is precisely where the Model Context Protocol (MCP) enters — as both a cognitive infrastructure and an interpretability framework. This article presents a deep dive into how Claude and MCP combine to create intelligent agents that not only respond effectively but reason responsibly. MCP acts as the neural scaffolding that surrounds Claude’s language intelligence, providing session continuity, external knowledge routing, and post-inference entropy scoring. The outcome is an AI system capable of behaving with epistemic caution — knowing when it knows, and when it doesn’t. Claude’s linguistic strength lies in its instruction-following capabilities, ethical alignment, and ability to handle long-form tasks. However, Claude, like most LLMs, lacks state persistence and formalized belief tracking. It generates responses from scratch on every prompt. MCP changes this by embedding the model in a context-controlled cognitive loop. The workflow begins with the Client… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI
↧
