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原文摘要

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.