Academic

HumanMCP: A Human-Like Query Dataset for Evaluating MCP Tool Retrieval Performance

arXiv:2602.23367v1 Announce Type: new Abstract: Model Context Protocol (MCP) servers contain a collection of thousands of open-source standardized tools, linking LLMs to external systems; however, existing datasets and benchmarks lack realistic, human-like user queries, remaining a critical gap in evaluating the tool usage and ecosystems of MCP servers. Existing datasets often do contain tool descriptions but fail to represent how different users portray their requests, leading to poor generalization and inflated reliability of certain benchmarks. This paper introduces the first large-scale MCP dataset featuring diverse, high-quality diverse user queries generated specifically to match 2800 tools across 308 MCP servers, developing on the MCP Zero dataset. Each tool is paired with multiple unique user personas that we have generated, to capture varying levels of user intent ranging from precise task requests, and ambiguous, exploratory commands, reflecting the complexity of real-world

S
Shubh Laddha, Lucas Changbencharoen, Win Kuptivej, Surya Shringla, Archana Vaidheeswaran, Yash Bhaskar · · 1 min read · 9 views
#cs.AI #cs.IR

arXiv:2602.23367v1 Announce Type: new Abstract: Model Context Protocol (MCP) servers contain a collection of thousands of open-source standardized tools, linking LLMs to external systems; however, existing datasets and benchmarks lack realistic, human-like user queries, remaining a critical gap in evaluating the tool usage and ecosystems of MCP servers. Existing datasets often do contain tool descriptions but fail to represent how different users portray their requests, leading to poor generalization and inflated reliability of certain benchmarks. This paper introduces the first large-scale MCP dataset featuring diverse, high-quality diverse user queries generated specifically to match 2800 tools across 308 MCP servers, developing on the MCP Zero dataset. Each tool is paired with multiple unique user personas that we have generated, to capture varying levels of user intent ranging from precise task requests, and ambiguous, exploratory commands, reflecting the complexity of real-world interaction patterns.

Executive Summary

This paper introduces the HumanMCP dataset, a large-scale, high-quality dataset of user queries designed to evaluate the performance of Model Context Protocol (MCP) tool retrieval. The dataset features diverse user queries generated to match 2800 tools across 308 MCP servers, addressing a critical gap in existing datasets and benchmarks. The HumanMCP dataset is a significant improvement over existing datasets, which often contain tool descriptions but fail to represent realistic user queries. This dataset has the potential to more accurately evaluate the tool usage and ecosystems of MCP servers, leading to better tool development and more effective benchmarking. However, the dataset's scope and limitations must be carefully considered when applying it in real-world scenarios.

Key Points

  • ▸ The HumanMCP dataset is a large-scale, high-quality dataset of user queries designed to evaluate MCP tool retrieval.
  • ▸ The dataset features diverse user queries generated to match 2800 tools across 308 MCP servers.
  • ▸ The HumanMCP dataset addresses a critical gap in existing datasets and benchmarks.

Merits

Strength in Realism

The HumanMCP dataset is designed to mimic realistic user queries, providing a more accurate representation of how users interact with MCP tools.

Improved Generalizability

The dataset's diverse user queries and tool coverage improve the generalizability of MCP tool evaluation, allowing for more accurate benchmarking and tool development.

Demerits

Scope Limitations

The HumanMCP dataset is limited to MCP servers and tools, potentially restricting its applicability to other tool retrieval contexts.

Data Generation Challenges

Generating high-quality, diverse user queries can be a challenging task, requiring significant resources and expertise.

Expert Commentary

The HumanMCP dataset is a significant contribution to the field of AI and tool development. Its focus on realistic user queries and diverse tool coverage makes it a valuable resource for evaluating MCP tool retrieval performance. However, its limitations, particularly with regards to scope and data generation challenges, must be carefully considered. Future research should focus on expanding the dataset's scope and developing more effective methods for generating high-quality user queries.

Recommendations

  • ✓ Develop and expand the HumanMCP dataset to include more tools, servers, and user queries.
  • ✓ Explore the use of the HumanMCP dataset in other tool retrieval contexts, such as web search or recommendation systems.

Sources

Related Articles