ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
- URL: http://arxiv.org/abs/2605.10787v2
- Date: Wed, 20 May 2026 01:39:17 GMT
- Title: ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
- Abstract summary: $textbfComplexMCP$ is a benchmark designed to evaluate agents in rigorous conditions.<n>Built on the Model Context Protocol (MCP), $textbfComplexMCP$ provides over 300 meticulously tested tools.
- Score: 61.862814740220806
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Current LLM agents are proficient at calling isolated APIs but struggle with the "last mile" of commercial software automation. In real-world scenarios, tools are not independent; they are atomic, interdependent, and prone to environmental noise. We introduce $\textbf{ComplexMCP}$, a benchmark designed to evaluate agents in these rigorous conditions. Built on the Model Context Protocol (MCP), $\textbf{ComplexMCP}$ provides over 300 meticulously tested tools derived from 7 stateful sandboxes, ranging from office suites to financial systems. Unlike existing datasets, our benchmark utilizes a seed-driven architecture to simulate dynamic environment states and unpredictable API failures, ensuring a deterministic yet diverse evaluation. We evaluate various LLMs across full-context and RAG paradigms, revealing a stark performance gap: even top-tier models fail to exceed a 60% success rate, far trailing human performance 90%. Granular trajectory analysis identifies three fundamental bottlenecks: (1) $\textbf{tool retrieval saturation}$ as action spaces scale; (2) $\textbf{over-confidence}$, where agents skip essential environment verifications; and (3) $\textbf{strategic defeatism}$, a tendency to rationalize failure rather than pursuing recovery. These findings underscore the insufficiency of current agents for interdependent workflows, positioning $\textbf{ComplexMCP}$ as a critical testbed for the next generation of resilient autonomous systems.
Related papers
- DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows [69.12339622053588]
We introduce DuMateBench, a real-session benchmark reconstructed from anonymized and privacy-screened user sessions.<n>The resulting benchmark comprises 200 tasks spanning 8 broad scenarios and 17 fine-grained capability categories.<n>We execute these tasks in isolated Docker containers injected with three forms of real-world environmental complexity: Insufficient, Unstable, and Noisy.
arXiv Detail & Related papers (2026-08-27T02:36:23Z) - RESTOR: Automated Test Oracle Generation for RESTful APIs via Reinforcement Learning [21.927343858709122]
Restor is a framework that generates executable test assertions from a single observed request-response pair in a black-box setting.<n>We evaluate Restor on an industrial dataset comprising over 2,300 API traces across 246 real-world services.
arXiv Detail & Related papers (2026-07-27T03:20:28Z) - AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities [55.23913617185508]
Agent is an open-source, lightweight, and infrastructure for evaluating Large Language Models (LLMs)-based agents.<n>It organizes the evaluation process around three independent components, namely Benchmark, Harness and Environment.<n>It features a fault-tolerant runtime and comprehensive trajectory analysis tools to transparently diagnose failure modes like reward-hacking.
arXiv Detail & Related papers (2026-07-15T11:11:17Z) - When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents [48.32450507410869]
Existing benchmarks evaluate Tool-Integrated Reasoning in LLMs on idealized ''happy paths''<n>We introduce ToolMaze, a benchmark for dynamic path discovery and error recovery in TIR agents.
arXiv Detail & Related papers (2026-06-04T07:38:46Z) - Toward Autonomous SOC Operations: End-to-End LLM Framework for Threat Detection, Query Generation, and Resolution in Security Operations [1.7102338932907293]
Security Operations Centers (SOCs) face mounting operational challenges.<n>These challenges come from increasing threat volumes, heterogeneous SIEM platforms, and time-consuming manual triage.<n>We present an end-to-end threat management framework that integrates ensemble-based detection, syntax-constrained query generation, and retrieval-augmented resolution support.
arXiv Detail & Related papers (2026-04-30T02:06:46Z) - Dynamic analysis enhances issue resolution [53.50448142467294]
DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
arXiv Detail & Related papers (2026-03-23T14:48:54Z) - LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks [4.6880826836662814]
We introduce textbfLOGIGEN, a logic-driven framework that synthesizes verifiable training data.<n>On $2$-Bench, LOGIGEN-32B(RL) achieves a textbf79.5% success rate, substantially outperforming the base model.
arXiv Detail & Related papers (2026-02-28T08:35:30Z) - MagicAgent: Towards Generalized Agent Planning [73.21129030631421]
We present textbfMagicAgent, a series of foundation models specifically designed for generalized agent planning.<n>We introduce a lightweight and scalable synthetic data framework that generates high-quality trajectories across diverse planning tasks.<n>We show that MagicAgent-32B and MagicAgent-30B-A3B achieve superior performance across diverse open-source benchmarks.
arXiv Detail & Related papers (2026-02-22T01:39:16Z) - Managing the Stochastic: Foundations of Learning in Neuro-Symbolic Systems for Software Engineering [0.27195102129094995]
Current approaches to AI coding agents blur the lines between the Large Language Model and the agent itself.<n>This paper proposes setting the control boundary such that the LLM is treated as a component of the environment environment.
arXiv Detail & Related papers (2025-12-18T15:28:21Z) - MSC-Bench: A Rigorous Benchmark for Multi-Server Tool Orchestration [0.0]
MSC-Bench is a large-scale benchmark for evaluating multi-hop, end-to-end tool orchestration by LLM agents.<n>It addresses gaps by constructing ground truth through 'equal function sets', allowing objective metrics such as F1 score.<n>It systematically tests agent capabilities from single-tool orchestration to complex cross-server planning, and robustness to out-of-scope requests.
arXiv Detail & Related papers (2025-10-22T09:45:11Z) - MSCoRe: A Benchmark for Multi-Stage Collaborative Reasoning in LLM Agents [7.339769470891067]
MSCoRe is a novel benchmark comprising 126696 domain-specific QA instances spanning scenarios in automotive, pharmaceutical, electronics, and energy sectors.<n>The commercial models performed best across all tasks and scenarios, but a notable gap in ROUGE scores remains between simple and complex tasks.<n>MSCoRe provides a valuable new resource for the community to evaluate and improve multi-stage reasoning in LLM agents.
arXiv Detail & Related papers (2025-09-22T11:36:16Z) - MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models [76.72220653705679]
We introduce MCPEval, an open-source framework that automates end-to-end task generation and deep evaluation of intelligent agents.<n> MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines.<n> Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance.
arXiv Detail & Related papers (2025-07-17T05:46:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.