@inproceedings{liu-zhang-2026-sgvef,
title = "{SGVEF}-{LOOP}: Coverage-Guided Progressive Topological Exploration and Fact-Grounded Metamorphic Evaluation for {MCP} Agents",
author = "Liu, Zenghao and
Zhang, Yansong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1224/",
pages = "26573--26599",
ISBN = "979-8-89176-390-6",
abstract = "The rapid expansion of the Model Context Protocol (MCP) ecosystem introduces a combinatorially complex tool space, rendering existing frameworks inadequate for comprehensive agent evaluation. To address this problem, we propose SGVEF-LOOP, a coverage-guided framework for progressive topological exploration and fact-augmented metamorphic testing. SGVEF operates via a synergistic closed loop: it navigates sparse regions using adaptive sampling, synthesizes oracle-free metamorphic pairs grounded in static knowledge, enforces dual-constraint validation to ensure consistency and solvability, and leverages execution feedback to iteratively optimize exploration. Deploying this framework yields a high-fidelity benchmark achieving 100{\%} node coverage and saturating 80.54{\%} of the theoretical transition bound (estimated via Chao1). Evaluation of 8 diverse MCP Agents reveals capability stratification and exposes critical behavioral anomalies{---}such as reasoning instability{---}that conventional metrics fail to capture. Consequently, this work establishes a generalizable paradigm for scalable, rigorous agent evaluation in dynamic environments."
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<abstract>The rapid expansion of the Model Context Protocol (MCP) ecosystem introduces a combinatorially complex tool space, rendering existing frameworks inadequate for comprehensive agent evaluation. To address this problem, we propose SGVEF-LOOP, a coverage-guided framework for progressive topological exploration and fact-augmented metamorphic testing. SGVEF operates via a synergistic closed loop: it navigates sparse regions using adaptive sampling, synthesizes oracle-free metamorphic pairs grounded in static knowledge, enforces dual-constraint validation to ensure consistency and solvability, and leverages execution feedback to iteratively optimize exploration. Deploying this framework yields a high-fidelity benchmark achieving 100% node coverage and saturating 80.54% of the theoretical transition bound (estimated via Chao1). Evaluation of 8 diverse MCP Agents reveals capability stratification and exposes critical behavioral anomalies—such as reasoning instability—that conventional metrics fail to capture. Consequently, this work establishes a generalizable paradigm for scalable, rigorous agent evaluation in dynamic environments.</abstract>
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%0 Conference Proceedings
%T SGVEF-LOOP: Coverage-Guided Progressive Topological Exploration and Fact-Grounded Metamorphic Evaluation for MCP Agents
%A Liu, Zenghao
%A Zhang, Yansong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-zhang-2026-sgvef
%X The rapid expansion of the Model Context Protocol (MCP) ecosystem introduces a combinatorially complex tool space, rendering existing frameworks inadequate for comprehensive agent evaluation. To address this problem, we propose SGVEF-LOOP, a coverage-guided framework for progressive topological exploration and fact-augmented metamorphic testing. SGVEF operates via a synergistic closed loop: it navigates sparse regions using adaptive sampling, synthesizes oracle-free metamorphic pairs grounded in static knowledge, enforces dual-constraint validation to ensure consistency and solvability, and leverages execution feedback to iteratively optimize exploration. Deploying this framework yields a high-fidelity benchmark achieving 100% node coverage and saturating 80.54% of the theoretical transition bound (estimated via Chao1). Evaluation of 8 diverse MCP Agents reveals capability stratification and exposes critical behavioral anomalies—such as reasoning instability—that conventional metrics fail to capture. Consequently, this work establishes a generalizable paradigm for scalable, rigorous agent evaluation in dynamic environments.
%U https://aclanthology.org/2026.acl-long.1224/
%P 26573-26599
Markdown (Informal)
[SGVEF-LOOP: Coverage-Guided Progressive Topological Exploration and Fact-Grounded Metamorphic Evaluation for MCP Agents](https://aclanthology.org/2026.acl-long.1224/) (Liu & Zhang, ACL 2026)
ACL