Adaptive Model Context Protocols for Multi-Agent Collaboration

Abstract

A new framework for adaptive model context protocols that improve multi-agent cooperation in dispersed environments is presented in this study. The suggested method makes use of dynamic context shar- ing mechanisms that adjust to task complexity, communication band- width, and computational limitations. The framework allows agents to negotiate the best parameters for information exchange by implementing a hierarchical context model with bidirectional context flow. In compar- ison to static approaches, the adaptive protocol lowers communication overhead while preserving task performance, as demonstrated by exper- imental evaluation in distributed sensor networks, autonomous vehicle coordination, and collaborative problem-solving. In order to intelligently filter information exchange, the framework presents context relevance scoring and selective propagation techniques. By providing solutions for autonomous systems functioning under fluctuating resource constraints, this research fills the gap between multi-agent collaboration and dis- tributed systems optimization.