arXiv:2508.15760v2 Announce Type: replace Abstract: Tool calling has emerged as a critical capability for AI agents. In contrast to conventional tool calling frameworks that rely on static, provider-specific tool definitions, the Model Context Protocol (MCP) offers a unified interface to discover and invoke tools dynamically. However, there is a significant gap in benchmarking multi-step tasks using diverse MCP tools in realistic, dynamic scenarios. In this work, we present LiveMCP-101, a benchmark of 101 real-world queries that require coordinated use of multiple MCP tools. To address tempora
The rapid advancement of AI agents and tool-calling capabilities necessitates robust benchmarking to ensure reliable and dynamic performance in real-world scenarios, which MCP aims to standardize.
Standardized benchmarking of multi-step tool-use for AI agents is critical for accelerating the development, adoption, and reliability of autonomous systems that perform complex tasks.
The introduction of LiveMCP-101 provides a common framework for stress-testing and diagnosing AI agents using a unified protocol, moving beyond static, provider-specific tool definitions.
- · AI agent developers
- · Enterprises adopting AI agents
- · AI platform providers
- · Proprietary tool-calling frameworks
- · Manual process automation companies
Improved performance and reliability of AI agents across diverse applications due to standardized testing.
Increased competition among AI agent developers as capabilities become more transparent and comparable.
Accelerated integration of sophisticated AI agents into critical workflows, leading to broader economic impact.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL