@inproceedings{song-etal-2026-help,
title = "Help or Hurdle? Rethinking Model Context Protocol-Augmented Large Language Models",
author = "Song, Wei and
Zhong, Haonan and
Ding, Ziqi and
Xing, Zhenchang and
Sui, Yulei and
Xue, Jingling and
Li, Yuekang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1911/",
pages = "43537--43553",
ISBN = "979-8-89176-459-0",
abstract = "The Model Context Protocol (MCP) enables large language models (LLMs) to access external resources on demand. While commonly assumed to enhance performance, how LLMs actually leverage this capability remains poorly understood. We introduce MCPGAUGE, the first comprehensive evaluation framework for probing LLM{--}MCP interactions along four key dimensions: proactivity (self-initiated tool use), compliance (adherence to tool-use instructions), effectiveness (task performance post-integration), and overhead (computational cost incurred). MCPGAUGE comprises a 160-prompt suite and 25 datasets spanning knowledge comprehension, general reasoning, and code generation. Our large-scale evaluation, spanning six commercial LLMs, 30 MCP tool suites, and both one- and two-turn interaction settings{---}comprises around 20,000 API calls and over USD{~}6,000 in computational cost. This comprehensive study reveals four key findings that challenge prevailing assumptions about the effectiveness of MCP integration. These insights highlight critical limitations in current AI{--}tool integration and position MCPGAUGE as a principled benchmark for advancing controllable, tool-augmented LLMs."
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%0 Conference Proceedings
%T Help or Hurdle? Rethinking Model Context Protocol-Augmented Large Language Models
%A Song, Wei
%A Zhong, Haonan
%A Ding, Ziqi
%A Xing, Zhenchang
%A Sui, Yulei
%A Xue, Jingling
%A Li, Yuekang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F song-etal-2026-help
%X The Model Context Protocol (MCP) enables large language models (LLMs) to access external resources on demand. While commonly assumed to enhance performance, how LLMs actually leverage this capability remains poorly understood. We introduce MCPGAUGE, the first comprehensive evaluation framework for probing LLM–MCP interactions along four key dimensions: proactivity (self-initiated tool use), compliance (adherence to tool-use instructions), effectiveness (task performance post-integration), and overhead (computational cost incurred). MCPGAUGE comprises a 160-prompt suite and 25 datasets spanning knowledge comprehension, general reasoning, and code generation. Our large-scale evaluation, spanning six commercial LLMs, 30 MCP tool suites, and both one- and two-turn interaction settings—comprises around 20,000 API calls and over USD 6,000 in computational cost. This comprehensive study reveals four key findings that challenge prevailing assumptions about the effectiveness of MCP integration. These insights highlight critical limitations in current AI–tool integration and position MCPGAUGE as a principled benchmark for advancing controllable, tool-augmented LLMs.
%U https://aclanthology.org/2026.emnlp-main.1911/
%P 43537-43553
Markdown (Informal)
[Help or Hurdle? Rethinking Model Context Protocol-Augmented Large Language Models](https://aclanthology.org/2026.emnlp-main.1911/) (Song et al., EMNLP 2026)
ACL