Enhancing Multi-Agent Collaboration through Model Context Protocol (MCP) for Context-Aware AI Systems
Keywords:
Model Context Protocol, multi-agent systems, context synchronization, autonomous agentsAbstract
This research analyses how the Model Context Protocol (MCP) consolidates communication and context-management to increase multi-agent ecosystem cooperation among autonomous AI agents. Traditional message-passing architectures have fragmented state representation, redundant information sharing, and increasing context drift during long-horizon activities. This paper presents an MCP-enabled architecture for dynamic, fine-grained, and continuously synchronised context sharing across computationally specialised agents including reasoning, data-access, and task-execution agents to maintain a common situational model with high temporal coherence. A case study of an autonomous research team comprising planner, coder, and validator agents controlled by MCP reveals how standardised context transmission promotes inter-agent dependency management and adaptive task-switching. Compared to message-passing systems, distributed reasoning pipelines improve task accuracy, context fragmentation, and robustness. MCP supports next-generation context-aware multi-agent intelligence systems, according to the findings.
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Copyright (c) 2025 Jaswinder Singh (Author)
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