MCP-Bench: New Benchmark with 250 Tools Exposes Persistent LLM Agent Planning Gaps

Accenture researchers, in collaboration with UC Berkeley, have introduced MCP-Bench, a novel benchmark designed to rigorously evaluate large language model (LLM) agents on complex, real-world tool-use tasks. Released recently on arXiv, this comprehensive evaluation framework connects LLMs to 28 live Model Context Protocol (MCP) servers, encompassing 250 distinct tools across diverse domains such as finance, scientific computing, and academic research. The initiative aims to address limitations of previous evaluations by simulating authentic, multi-step workflows that mirror actual user needs.

Unlike earlier API-based benchmarks that often relied on isolated functionalities or explicit tool specifications, MCP-Bench emphasizes realistic, multi-hop execution trajectories and cross-domain orchestration. According to the research paper, it specifically tests agents' ability to "retrieve relevant tools from fuzzy instructions without explicit tool names" and to "ground responses in intermediate tool outputs." This innovative approach forces LLM agents to infer appropriate tools and plans from natural, sometimes vague, user requests, mimicking genuine assistant scenarios.

The benchmark's extensive ecosystem includes tools for biomedical research, travel planning, unit conversion, and more, enabling the construction of intricate tasks that require chaining heterogeneous tools. Tasks are automatically synthesized to be challenging, often demanding multi-goal and multi-hop planning over multiple interaction rounds. This systematic design is intended to expose critical weaknesses in current LLM agents, particularly in long-horizon planning and cross-server coordination.

Initial experiments on 20 advanced LLMs, including top-tier models like GPT-5, OpenAI's o3, and Anthropic's Claude 4 Sonnet, revealed persistent challenges. While models generally demonstrated high schema understanding and basic tool execution, significant disparities emerged in higher-level reasoning. The study found that "planning effectiveness" remains a key differentiator, with even leading models struggling with dependency awareness and parallelism, especially as task complexity increases in multi-server environments. Smaller models showed noticeable degradation in performance when transitioning from single-server to multi-server scenarios.

Developed by Zhenting Wang et al., MCP-Bench provides a crucial, standardized platform for advancing agentic LLM capabilities. By bridging the gap between theoretical benchmarks and practical, real-world applications, it offers valuable insights for researchers and developers aiming to build more robust and intelligent AI agents capable of handling the nuances and complexities of human-like problem-solving. The code and data for MCP-Bench are publicly available on GitHub.