MICA 研究：能力相同时 MCP 与 CLI 接口仍导致语言智能体行为差异
Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language Agents
AI 导读
一项受控研究提出 MICA 框架，在共享语义动作契约下对比 MCP 与 CLI 接口对语言智能体行为的影响。在六个模型、三个交互环境中，接口差异导致 94.3% 的 2,004 条轨迹出现结构性分歧，并影响任务表现与交互成本。配套的 DART 方法在全部六个设置中提升基准分数最多 9.0 分，并将 MCP token 成本每任务最多降低 27.1k tokens。
正文 · 原文
Abstract:Language agents increasingly act through external tools and interactive environments, making the interface between the model and its executable capabilities part of the decision process. Model Context Protocol (MCP) and command-line interfaces (CLI) can expose the same semantic operations through markedly different model-facing representations. We study whether these representational differences change agent behavior when executable capabilities are matched. We introduce Matched Interface Comparison and Analysis (MICA), which renders MCP and CLI from a shared semantic action contract and routes decoded actions through the same runtime. Across six models and three interactive environments, matched interfaces produce substantial differences in task performance and interaction cost. These effects extend beyond surface validity to semantic action selection, failure pathways, and recovery. Matched-state probes localize sensitivity to capability presentation, realization, and feedback. Paired natural trajectories reveal structured divergence in 94.3% of 2,004 episodes and show that early differences propagate through subsequent interaction. This analysis identifies interface realization as a direct intervention boundary. We therefore introduce Decoupled Action Realization and Targeted Repair (DART), which preserves validated semantic commitments while repairing interface-specific realization. DART improves benchmark score in all six evaluated settings by up to 9.0 points and reduces MCP token cost in all three environments by up to 27.1k tokens per task. Together, these results establish interface representation as part of the closed-loop decision environment shaping agent behavior.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34298 [cs.AI] |
| (or arXiv:2610.34298v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34298 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhenning Shi [view email]
[v1]
Sun, 25 Oct 2026 13:54:07 UTC (551 KB)
来源：arXiv cs.AI · arxiv.org