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arXiv AI By Xuanang Gao, Kepeng Lin, Xinyan Shi, Yong Deng, Shuheng Shen, Changhua Meng, Xiaofeng Wu, Jie Yang, Wei Liu

EvoMCP: Co-evolving MCP agents and training data

Read the original on arXiv AI →

EvoMCP introduces a mutual evolution framework that co‑evolves Model Context Protocol (MCP) agents and their training data. The method iteratively assesses an agent’s strengths and gaps, generates new tasks by adapting verified seed tasks, refines demonstrations through trajectory evolution, and fine‑tunes the agent with these task‑trajectory pairs. Experiments on Toolathlon, MCPMark, and MCP‑Atlas show consistent performance gains of 16.5%, 10.2%, and 12.6% respectively.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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