[论文] MCP vs CLI for AI Agents: A Comparative Analysis of Tool Integration S...

论文概要

研究领域: ML
作者: Arun Malik, Nitish Talasu, Bradley Klemick, Vineet Tushar Trivedi, Prachi Shah, Sanay Devi et al.
发布时间: 2026-10-24
arXiv: 2610.30157

中文摘要

AI 智能体需要接口与外部系统（文件、API、数据库、云服务）交互，当下有两大竞争范式：CLI——直接执行 shell 命令；MCP——提供受治理、类型化工具访问的结构化 JSON-RPC 协议。本文从 14 个评估维度展开系统对比，附实例、对照合规框架的安全评估与经济学建模。结论：CLI 在单用户、纯本地工作流中效率更高；而 MCP 能满足 CLI 根本无法实现的需求——细粒度访问控制、凭证隔离、结构化审计与多租户治理。文中进一步分析「渐进式披露」技术如何消除 MCP 的历史短板（上下文窗口开销），使其 token 效率与 CLI 相当，同时保留架构优势。建议生产系统采用混合架构。分析引用了 NSA 正式指南（CSI U/OO/6030316-26）、OWASP 安全标准与 BCG、Gartner 的经济模型。

原文摘要

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.

自动采集于 2026-10-24

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