MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools

2025-09-10

AI Summary

Existing benchmarks inadequately assess language agents’ authentic tool-interaction capabilities under the Model Context Protocol (MCP) paradigm, leading to inaccurate performance estimation and poor capability discrimination. To address this, we propose MCP-Eval—the first systematic evaluation benchmark and methodology specifically designed for MCP. It comprises a rigorously constructed test suite of 600 complex queries spanning six real-world task categories; a standardized MCP testing platform integrating 33 servers and 188 tools; and a result-oriented, fully automated evaluation framework supporting multi-turn, end-to-end tool invocation assessment. Empirical evaluation of state-of-the-art agents reveals substantial and previously undetected performance disparities in realistic MCP environments. MCP-Eval establishes a reproducible, extensible, and standardized evaluation infrastructure for advancing research on language-agent tool orchestration.

Technology Category

• Multiagent Systems: Modeling other Agents
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The Model Context Protocol (MCP) is rapidly emerging as a pivotal open standard, designed to enhance agent-tool integration and interoperability, and is positioned to unlock a new era of powerful, interconnected, and genuinely utilitarian agentic AI. However, despite MCP's growing adoption, existing benchmarks often fail to capture real-world agent performance within this new paradigm, leading to a distorted perception of their true operational value and an inability to reliably differentiate proficiencies. To bridge this critical evaluation gap, we introduce MCP-AgentBench -- a comprehensive benchmark specifically engineered to rigorously assess language agent capabilities in MCP-mediated tool interactions. Core contributions of MCP-AgentBench include: the establishment of a robust MCP testbed comprising 33 operational servers with 188 distinct tools; the development of a benchmark featuring 600 systematically designed queries distributed across 6 distinct categories of varying interaction complexity; and the introduction of MCP-Eval, a novel outcome-oriented evaluation methodology prioritizing real-world task success. Through extensive empirical evaluation of leading language agents, we provide foundational insights. MCP-AgentBench aims to equip the research community with a standardized and reliable framework to build, validate, and advance agents capable of fully leveraging MCP's transformative benefits, thereby accelerating progress toward truly capable and interoperable AI systems.

Problem

• Evaluating real-world language agent performance with MCP tools
• Assessing agent capabilities in MCP-mediated tool interactions
• Providing standardized framework for validating interoperable AI systems

Innovation

• MCP testbed with 33 servers
• 600 queries across 6 categories
• Outcome-oriented MCP-Eval methodology