What Model Context Protocol gives your agents & what it leaves to you

The Model Context Protocol (MCP) is an open standard designed to streamline the connection of AI agents to external tools and data, functioning like a universal connector similar to a USB-C port for AI applications. While MCP effectively standardizes how agents communicate with tools using JSON-RPC 2.0 messages, it does not address aspects like memory, data freshness, or reliability, which remain the responsibility of developers to manage. These limitations mean that agent memory, typically split into short-term and long-term memory, context freshness, and the reliability of long agent chains require separate solutions, often involving real-time data layers like Redis. Redis offers capabilities such as vector search, semantic caching, and real-time coordination to bridge the gaps left by MCP, ensuring that agents can maintain context, update data promptly, and function reliably across complex workflows. Redis Iris, in particular, acts as a context engine, integrating seamlessly with MCP to provide agents with the necessary context, memory, and retrieval functionalities, thereby enhancing the overall performance and reliability of AI systems.

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