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

Authors: Arun Malik, Nitish Talasu, Bradley Klemick, Vineet Tushar Trivedi, Prachi Shah, Sanay Devi, Junjie Zhang, Kunal Sonawane

arXiv: 2610.30157 [cs.AI] — Submitted 29 July 2026

Subjects: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Software Engineering (cs.SE)

ACM Classes: I.2.11; D.2.11; K.6.5

Comments: 9 pages, 6 figures

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, a 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.

Publication Details

• Cite as: arXiv:2610.30157v1 [cs.AI]
• Full text: Available via arXiv

Key Contributions at a Glance

1. Fourteen-dimension comparison of CLI and MCP as tool-integration strategies for autonomous AI agents.
2. Worked examples illustrating both paradigms in practice.
3. Security assessment mapped to established compliance frameworks, including NSA guidance and OWASP standards.
4. Economic modeling informed by BCG and Gartner analyses.
5. Progressive disclosure as a technique that neutralizes MCP's context-window overhead, achieving token efficiency comparable to CLI.
6. Hybrid architecture recommendation for production deployments that balances CLI efficiency with MCP governance.



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