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About this paper
Chaithanya Bandi, Razvan-Gabriel Dumitru, Ben Hertzberg, Divyansh Agarwal, Geobio Boo, Tejas Polakam, Sami Hassaan, Jeff Da and 15 more
MCP-Atlas is a large-scale benchmark comprising 1,000 real-world tasks designed to evaluate the tool-use capabilities of large language model agents against 36 live production servers and 220 tools. The study revealed a three-tier performance hierarchy among 20 models, with cognitive failures (63.3%) being a more prevalent bottleneck for frontier models than tool-calling issues (36.7%).
Key insights
- For frontier LLM agents, the primary performance bottleneck in tool-use tasks has shifted from mechanical tool-calling errors to higher-level cognitive failures such as faulty synthesis or early termination.
- Open-source models are demonstrating competitive performance on complex agentic tasks, narrowing the gap with leading proprietary models.
- Practical deployment considerations involve a clear trade-off between an agent's accuracy and its execution latency, defining an efficiency Pareto frontier.
Summary by alphaXiv.
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