MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers
Merged summary
TL;DR - MCPEvol-Bench is a new benchmark that evaluates how well LLM agents adapt when Model Context Protocol (MCP) server tools evolve over time, a dimension existing tool-use benchmarks ignore. It matters because it exposes how brittle even frontier agents are when tool interfaces change in production.
- Introduces 11 mutation operators (informed by an empirical study) to simulate realistic tool evolution across 123 MCP servers, testing agents on multiple server versions.
- Benchmarks 12 state-of-the-art LLMs; even frontier models struggle to adapt to evolving toolsets.
- Reports performance declines of 13.7% (GPT-5.4) and 14.4% (Claude-Sonnet-4-6) on evolved servers, with notable rises in planning and reasoning errors.
- Positions the benchmark as a standard for assessing agent adaptability in dynamic tool environments, highlighting vulnerability of LLM-driven workflows.
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MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers
TL;DR - MCPEvol-Bench is a new benchmark that evaluates how well LLM agents adapt when Model Context Protocol (MCP) server tools evolve over time, a dimension existing tool-use benchmarks ignore. It matters because it exposes how brittle even frontier agents are when tool interfaces change in production.
- Introduces 11 mutation operators (informed by an empirical study) to simulate realistic tool evolution across 123 MCP servers, testing agents on multiple server versions.
- Benchmarks 12 state-of-the-art LLMs; even frontier models struggle to adapt to evolving toolsets.
- Reports performance declines of 13.7% (GPT-5.4) and 14.4% (Claude-Sonnet-4-6) on evolved servers, with notable rises in planning and reasoning errors.
- Positions the benchmark as a standard for assessing agent adaptability in dynamic tool environments, highlighting vulnerability of LLM-driven workflows.