What's the difference between MCP decision engine and RAG, and when should you use each?

The context defines MCP in four ways for the caller's project "Liminality": as a product, tool, framework, and system. It does not define a "decision engine" component within Liminality or its MCP. The question specifically asks about the "MCP decision engine."

The term "MCP" in the question could mean two things: 1. The Caller's MCP: The Liminality system described in the context. 2. The Industry Standard MCP: The Model Context Protocol (a standard for connecting AI models to tools).

The context explicitly instructs me to treat "MCP" as the caller's own product/framework/system. It does not provide any information about a "decision engine" within that system, nor does it provide a definition of how that "decision engine" operates compared to RAG.

The context is under-determined regarding the specific functionality of the "MCP decision engine" within the caller's Liminality architecture. It does not state: * What the "decision engine" does. * How it makes decisions. * Whether it uses retrieval, rules, or other mechanisms.

I cannot answer "What's the difference between [Caller's] MCP decision engine and RAG" because the definition of the former is missing from the provided context.

I must ask the user for the missing definition.

The provided context defines Liminality as a "question-decomposition and routing MCP server" but does not describe a specific "MCP decision engine" component. Could you please clarify what the "MCP decision engine" refers to in your architecture? Specifically: 1. Does it refer to the core routing logic of Liminality? 2. Or is it a distinct module that uses retrieval, rules, or other methods to make decisions? 3. How does it currently process or select information compared to a standard RAG (Retrieval-Augmented Generation) pipeline?

Once you provide this definition, I can compare it to RAG based on your specific implementation.