Model Context Protocol (MCP) is a conceptual and technical framework designed to manage how information is introduced, maintained, and updated within @Large Language Model (LLM) interactions. It specifies methods for structuring prompts, memory objects, and conversational threads to maintain coherence, relevance, and user intent across sessions. MCP integrates principles from context management, @Retrieval-Augmented Generation (RAG), and dynamic memory systems, ensuring that both transient and persistent information are appropriately weighted. This protocol supports scalable architectures where individual agents can adapt to evolving user needs while preserving continuity and accuracy in long-term engagements. Think of MCP as APIs for AI and RAG as memory systems, it’s different process. MCP is almost like a tool protocol for AI to explore your mythos, like “get notes” “write note” “add attachment”. RAG is the process AI uses to take tons of information, break it into chunks and then use “vector math” to determine how semantically close each chunk is to each other. So you say dog and it finds everything similar to a dog in its database. Lately though people have found limitations to RAG, so now they use graph search (relationship search between entities), BM25 search (pure keyword) search with RAG to find things in “their brain” MCP often interacts with broader systems like @MythOS, which applies modular @memo structures for knowledge management.