MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models
This addresses the need for robust and scalable evaluation frameworks for AI agents, though it is incremental as it builds on existing protocols to automate processes.
The authors tackled the problem of evaluating LLM-based intelligent agents by introducing MCPEval, an automated framework that generates tasks and performs deep evaluation across domains, showing effectiveness in revealing nuanced performance in five real-world domains.
The rapid rise of Large Language Models (LLMs)-based intelligent agents underscores the need for robust, scalable evaluation frameworks. Existing methods rely on static benchmarks and labor-intensive data collection, limiting practical assessment. We introduce MCPEval, an open-source Model Context Protocol (MCP)-based framework that automates end-to-end task generation and deep evaluation of LLM agents across diverse domains. MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines. Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance. We publicly release MCPEval https://github.com/SalesforceAIResearch/MCPEval to promote reproducible and standardized LLM agent evaluation.