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.