MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models
MCPEval is an open-source, Model Context Protocol (MCP)-based framework designed for automated, deep evaluation of Large Language Model (LLM) agents across diverse domains. It automates end-to-end task generation, verification, and evaluation, standardizing metrics and seamlessly integrating with native agent tools. Empirical results across five real-world domains demonstrate its effectiveness in revealing nuanced, domain-specific performance and promoting reproducible LLM agent evaluation. ✨
Article Points:
1
MCPEval automates LLM agent evaluation, from task generation to deep analysis.
2
Open-source, MCP-based framework standardizes metrics & integrates agent tools.
3
Eliminates manual effort in evaluation pipeline construction.
4
Reveals nuanced, domain-specific performance across diverse real-world domains.
5
Collects detailed task trajectories for iterative agent model improvement.
6
Promotes reproducible and standardized LLM agent evaluation.
MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models
Purpose
Automate LLM agent evaluation
Standardize metrics
Eliminate manual effort
Framework
MCP-based system
Task Generation
Task Verification
Model Evaluation
Evaluation
10 LLM Models
5 Real-world Domains
Criteria::icon(fa fa-cogs)
- Tool Call Performance
- LLM Judger Performance
Results
GPT-4 variants lead
Smaller models perform well
Execution-Completion Gap
Domain-specific challenges
Impact