mlsanityData to 14 Oct 2026

ETOM: A Five-Level Benchmark for Evaluating Tool Orchestration within the MCP Ecosystem

arXiv 2510.19423 ↗First posted 22 Oct 2025cs.AIcited by 0

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

We introduce ETOM, a five-level benchmark for evaluating multi-hop, end-to-end tool orchestration by LLM agents within a hierarchical Model-Context Protocol (MCP) ecosystem. Existing benchmarks often assess tools in isolation, overlooking challenges such as functional overlap and cross-server orchestration, which can lead to overly optimistic evaluations. ETOM addresses these gaps by constructing ground truth through "equal function sets", enabling objective metrics such as F1 score and reducing reliance on LLM-as-a-judge evaluation. Its five-level curriculum systematically tests agent capabilities, from single-tool orchestration to complex cross-server planning, as well as robustness to out-of-scope requests. Experiments reveal that rigid hierarchies can hinder performance without co-designed strategies, and even state-of-the-art agents exhibit systemic weaknesses in robustness. ETOM provides a diagnostic framework to expose these limitations and guide the development of more capable and efficient tool-using agents.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 19 of its 73 references that are in the map. First posted on arXiv 22 Oct 2025.

Builds on

19 of 73 references.

Closest ancestors · 13

Same direction, newest first

  1. 2025LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries21 Aug
  2. 2025LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?3 Aug
  3. 2025MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models17 Jul
  4. 2025MCP-Zero: Active Tool Discovery for Autonomous LLM Agents1 Jun
  5. 2025MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Models22 May
  6. 2025StableToolBench-MirrorAPI: Modeling Tool Environments as Mirrors of 7,000+ Real-World APIs26 Mar
  7. 2025ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario17 Jan
  8. 2025ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use5 Jan
  9. 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases18 Oct
  10. 2024Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark14 May
  11. 2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls6 Feb
  12. 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use4 Oct
1 more
  1. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul

Other references · 6

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023GAIA: a benchmark for General AI Assistantscited by 1,128
  3. 2024JudgeBench: A Benchmark for Evaluating LLM-based Judgescited by 318
  4. 2025MultiAgentBench: Evaluating the Collaboration and Competition of LLM agentscited by 178
  5. 2025Preference Leakage: A Contamination Problem in LLM-as-a-judgecited by 125
  6. 2025SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?cited by 105

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  4. 2026MCP-GRANITE Benchmark: GRANularity Interface TEsting for MCP-Based LLM Agents
  5. 2026Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction
  6. 2026MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers
  7. 2026From Tool Orchestration to Code Execution: A Study of MCP Design Choices
  8. 2026Orchestral AI: A Framework for Agent Orchestration