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HumanMCP: A Human-Like Query Dataset for Evaluating MCP Tool Retrieval Performance
arXiv – CS AI|Shubh Laddha, Lucas Changbencharoen, Win Kuptivej, Surya Shringla, Archana Vaidheeswaran, Yash Bhaskar||20 views
🤖AI Summary
Researchers have released HumanMCP, the first large-scale dataset designed to evaluate tool retrieval performance in Model Context Protocol (MCP) servers. The dataset addresses a critical gap by providing realistic, human-like queries paired with 2,800 tools across 308 MCP servers, improving upon existing benchmarks that lack authentic user interaction patterns.
Key Takeaways
- →HumanMCP is the first large-scale dataset specifically designed for evaluating MCP tool retrieval performance with realistic user queries.
- →The dataset covers 2,800 tools across 308 MCP servers, significantly expanding evaluation capabilities.
- →Multiple user personas are generated for each tool to capture varying levels of intent from precise requests to ambiguous commands.
- →Existing datasets lack realistic human-like queries, leading to poor generalization in MCP tool evaluation.
- →The dataset builds upon the MCP Zero dataset to better reflect real-world interaction complexity.
Read Original →via arXiv – CS AI
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