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arXiv · 2610.30157

MCP vs CLI for AI Agents: A Comparative Analysis of Tool Integration Strategies for Autonomous Systems

Arun Malik·Nitish Talasu·Bradley Klemick·Vineet Tushar Trivedi·Prachi Shah·Sanay Devi·Junjie Zhang·Kunal Sonawane
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Abstract

AI agents require interfaces to interact with external systems such as files, APIs, databases, and cloud services. Two competing paradigms have emerged: CLI (Command Line Interface), where agents execute shell commands directly, and MCP (Model Context Protocol), a structured JSON-RPC protocol providing governed, typed tool access. This paper presents a comparative analysis across 14 evaluation dimensions, supported by worked examples, security assessment against established compliance frameworks, and economic modeling. Our analysis indicates that CLI provides superior efficiency for single-user, local-only workflows, while MCP addresses requirements that CLI fundamentally cannot satisfy, including fine-grained access control, credential isolation, structured audit, and multi-tenant governance. We further examine how progressive disclosure techniques eliminate MCP's historical weakness (context window overhead), yielding token efficiency comparable to CLI while preserving its architectural benefits. A hybrid architecture is recommended for production systems. This analysis draws upon academic, government, and industry sources including NSA formal guidance (CSI U/OO/6030316-26), OWASP security standards, and economic modeling from BCG and Gartner.

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Arun Malik, Nitish Talasu, Bradley Klemick, Vineet Tushar Trivedi, Prachi Shah, Sanay Devi, Junjie Zhang, Kunal Sonawane. 2026-07-29. MCP vs CLI for AI Agents: A Comparative Analysis of Tool Integration Strategies for Autonomous Systems. https://arxiv.org/abs/2610.30157

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