Model Context Protocol (MCP) Server for Multi-Agent AI Assistants with Persistent Memory: An Integrated Framework for Automated Workflow Management

Abstract

The growing complexity of intelligent systems has led to an increased use of multi-agent systems, yet current approaches are often limited by disjointed context management, absence of persistent memory, and poor workflow management. This research introduces a unified framework using the Model Context Protocol (MCP) to overcome these challenges by facilitating structured communication, contextual awareness, and automated workflow management among multiple agents. Our architecture includes a persistent memory component that uses semantic retrieval technologies to store and retrieve contextual information to avoid redundant processing and ensure decision consistency. A context manager was proposed to coordinate information between agents, ensuring consistency in task execution, and a workflow orchestrator to distribute tasks to agents according to their capabilities and system state. Several benchmark metrics, such as task completion rate, task execution time, resource consumption, and agent efficiency, are used to assess the framework's performance. The experiments show remarkable improvements over traditional single-agent and stateless multi-agent systems in terms of task completion, execution time, and resource allocation. The use of memory-based reasoning improves system scalability and flexibility, allowing it to manage complex, multi-step processes. The results show the need to integrate communication protocols with persistent memory and collaborative agent communication to create reliable, scalable, and intelligent automated systems that can tackle real-world problems.