How can MCP improve LLM multi-agent coordination?
Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications
This paper introduces the Model Context Protocol (MCP), a framework designed to improve multi-agent AI systems by standardizing how agents store, retrieve, and share context. This addresses the "disconnected models problem," where agents struggle to maintain coherent context across interactions.
Key MCP features for LLM-based multi-agent systems include: standardized context storage and retrieval beyond LLM context windows; context sharing between agents; context-aware tool use; contextual prioritization; support for long-term memory (episodic, semantic, procedural); and mechanisms for handling conflicting information. MCP leverages a client-server architecture where LLMs act as clients requesting information and tools from servers, fostering flexible deployments and independent evolution of system components. It also addresses security and interoperability using standardized primitives and message formats for inter-agent communication, potentially improving collaboration across diverse platforms and LLMs.