模型上下文协议安全工具参数优化OA

To address the inefficiency caused by fixed parameters in security tool integration within model context protocols,this paper proposes a context-aware parameter adaptive optimization method.The approach employs a three-layer optimization mechanism incorporating target type awareness,network latency adaptation,and historical data learning to dynamically adjust tool parameters.A 500-line Python optimization module was implemented on the HexStrike AI v6.0 platform for parameter optimization of security tools such as Nmap.Experiments show that this method reduces the average execution time from 315 seconds to 188 seconds,improving efficiency by 40.3%,while decreasing the standard deviation of execution time by 61.5%.The success rate increased from 90.0%to 97.5%,with Web server optimization time reduced by 75.6%and port hit rate improved by 50 times.