MCP-AgentBench: Evaluating Real-World Language Agent Performance with
  MCP-Mediated Tools
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.
68c76e07ee0eed1697d6b68a
Zikang Guo
admin_assigned
2025-09-15T15:22:46.250Z
66c6f02ee2fdf1d811ec5fbd
/avatars/dc029e6042d974742d1789e7e3c014f2.svg
zikang guo
user
xuekeluoluo
68c76e07ee0eed1697d6b68b
Benfeng Xu
68c76e07ee0eed1697d6b68c
Chiwei Zhu
admin_assigned
2025-09-15T15:23:07.060Z
663b22a80966eef8686aadaf
https://cdn-avatars.huggingface.co/v1/production/uploads/663b22a80966eef8686aadaf/iBzyQTyGZKf33RPVIFh9a.jpeg
Chiwei Zhu
user
IgnoraZ
68c76e07ee0eed1697d6b68d
Wentao Hong
68c76e07ee0eed1697d6b68e
Xiaorui Wang
68c76e07ee0eed1697d6b68f
Zhendong Mao
MCP-AgentBench: Evaluating Real-World Language Agent Performance with
  MCP-Mediated Tools
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.
MCP-AgentBench is a benchmark designed to evaluate language agents in MCP-mediated tool interactions, providing a standardized framework for assessing real-world performance.
MCP MCP-AgentBench MCP testbed MCP-Eval language agents tool interactions benchmark evaluation methodology real-world task success