arXiv:2602.00933v3 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is emerging as a standard interface through which large language model (LLM) agents discover and invoke external tools. However, existing MCP evaluations fall short along three key axes: realistic multi-step workflows with cross-server orchestration, breadth across authentic MCP servers rather than mocks, and structured, reproducible claim-level scoring disentangled from agent verbosity or style. We introduce MCP-Atlas, a benchmark for measuring tool-use competency against production MCP servers. MCP-Atl
The proliferation of LLM agent architectures and the emergent MCP standard necessitate more robust, real-world evaluation benchmarks to measure their practical efficacy.
This benchmark directly addresses critical shortcomings in current LLM agent evaluation, moving beyond theoretical or mocked environments to actual production-grade tool interaction, which is crucial for reliability and deployment.
The introduction of MCP-Atlas provides a standardized, rigorous framework for assessing LLM tool-use competency against genuine MCP servers, offering a clearer picture of agent capabilities and limitations.
- · AI Agent developers
- · Enterprises deploying AI agents
- · Tool developers for LLMs
- · AI evaluation platforms
- · Developers relying solely on synthetic benchmarks
- · Low-quality AI agent products
Improved evaluation leads to more robust and reliable AI agents capable of complex tasks.
Accelerated development and adoption of AI agents in enterprise and consumer applications due to increased trust and demonstrated capability.
The benchmark could become a de facto industry standard, shaping future AI agent design and MLOps practices.
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Read at arXiv cs.AI