Canary Tools: Diagnosing Tool-Selection Reasoning in LLM Agents
Score
78提出全新诊断方法和六维分类法，填补了工具选择推理归因研究的空白。
实验设计严谨，大样本多模型测试并双裁判验证，理论支撑扎实。
框架代码开源，可直接用于Agent开发中的工具选择诊断与改进。
针对当前LLM Agent工具调用热点的及时研究，方法具有前瞻性。
来自arXiv预印本，作者机构历史可信，但尚未经同行评审。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
Innovative methodology, rigorous experiments, and fully open resources, though synthetic probes may not fully capture real-world deployment complexity.
AI commentary
「This paper's value lies in breaking the black-box 'wrong tool' problem into six actionable defect types, giving agent developers a free diagnostic toolkit. The finding that cheaper models can be safer is a useful reminder not to blindly trust frontier models. If you debug MCP toolchains or assess agent safety, this is a must-read.」
Quick overview
This paper introduces Canary Tools, a method that plants diagnostic probe tools into an agent's MCP toolset to systematically identify six types of tool-selection reasoning failures: semantic decoys, parameter traps, capability mirages, prerequisite blindness, temporal decoys, and granularity traps. Evaluating 8 models (frontier and 8B open-weight) across 8,640 runs, the study finds a 36x variation in susceptibility; capability tier alone does not predict safety, with a mid-tier hosted model being most susceptible and a cheaper model sometimes safer. Capability mirages most reliably trap frontier models, while other failure types mostly affect small open models. Softening giveaway phrases leaves frontier performance unchanged, confirming the probes measure reasoning, not phrase-spotting.
- A six-type taxonomy converts single wrong-tool outcomes into multi-dimensional reasoning profiles
- Across 8 models and 8,640 runs, susceptibility varies 36x; mid-tier hosted models are most vulnerable
- Capability mirages trap frontier models best, while other weaknesses mainly fire on small open models
- Robust models show no significant degradation under canary pressure; susceptibility correlates with task failure
Full text
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