Abstract:

To address the issue that current research on unmanned swarm combat effectiveness evaluation generally relies on expert experience and preset rules, making it difficult to meet the intelligent and automated requirements of practical combat evaluation, an unmanned swarm combat effectiveness evaluation method based on model-context protocol is proposed. The method establishes a unified mathematical modeling framework for evaluation indicators and integrates components for natural language parsing, model scheduling, and data processing, supports users inputting evaluation requirements in text format. The agent autonomously selects evaluation models, configures data processing pipelines, and performs assessment tasks, ultimately generating multidimensional evaluation results. Experiments based on typical combat scenarios demonstrate that the proposed method has significantly advantages in evaluation accuracy, response efficiency, and interaction convenience. It effectively reduces human intervention and enhances the level of intelligent. The findings provide methodological and technical reference for building intelligent evaluation system for unmanned swarm combat effectiveness.

Key words: unmanned swarm combat, combat effectiveness evaluation, model-context protocol（MCP）, artificial intelligence（AI） agent