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LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
arXiv – CS AI|Guozhao Mo, Wenliang Zhong, Jiawei Chen, Qianhao Yuan, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun||7 views
🤖AI Summary
LiveMCPBench introduces the first large-scale benchmark evaluating AI agents' ability to navigate real-world tasks using Model Context Protocol (MCP) tools across multiple servers. The benchmark reveals significant performance gaps, with top model Claude-Sonnet-4 achieving 78.95% success while most models only reach 30-50%, identifying tool retrieval as the primary bottleneck.
Key Takeaways
- →LiveMCPBench evaluates 95 real-world tasks across 70 servers with 527 tools, addressing gaps in current AI agent evaluation methods.
- →Claude-Sonnet-4 leads with 78.95% task success rate while most state-of-the-art models achieve only 30-50% success.
- →Retrieval errors account for nearly half of all agent failures, making it the dominant performance bottleneck.
- →Active tool composition strongly correlates with task success, highlighting the importance of multi-tool coordination.
- →The benchmark provides the first reproducible, large-scale diagnosis of MCP agent capabilities with publicly available code and data.
#ai-agents#mcp#benchmark#tool-retrieval#llm-evaluation#multi-tool-composition#claude-sonnet#performance-gap#research
Read Original →via arXiv – CS AI
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