Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language Agents
Zhenning Shi, Liang Pang and Yongqi Li (The Hong Kong Polytechnic University, Institute of Computing Technology, Chinese Academy of Sciences) test whether exposing the same operations through MCP or a command-line interface changes agent behavior when capabilities are matched.
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MICA. Matched Interface Comparison and Analysis renders MCP and CLI from one shared semantic action contract and routes decoded actions through the same runtime, so only the model-facing representation differs.
Behavior differs. Across six models and three interactive environments, matched interfaces produce substantial differences in task score and interaction cost, extending to which actions agents choose, how they fail and how they recover.
Divergence. Paired trajectories diverge structurally in 94.3% of 2,004 episodes, and early differences propagate through the rest of the interaction.
DART. Decoupled Action Realization and Targeted Repair keeps validated semantic decisions while repairing interface-specific realization. It raises scores in all six settings by up to 9.0 points and cuts MCP token cost by up to 27.1k tokens per task.
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.