Abstract: Well-facilitated farmland construction is a strategic measure for strengthening national food security, and an efficient and accurate inspection and evaluation system is essential for improving the supervision of construction quality. However, conventional inspection approaches for well-facilitated farmland still rely heavily on manual field investigation and fragmented post-processing workflows. These approaches are often limited in task automation, end-to-end decision-making, and large-area monitoring efficiency, making it difficult to meet the growing demand for standardized, timely, and intelligent farmland management. To address these limitations, this study proposes an agent-based collaborative framework for intelligent inspection and evaluation of well-facilitated farmland. The proposed framework integrates multi-source “space–air–ground” perception data, multimodal large language models, domain-specific knowledge bases, and the Model Context Protocol. Through a task-chain-driven mechanism, the framework organizes the complete inspection workflow, including task understanding, subtask decomposition, data and tool invocation, spatial analysis, intelligent decision-making, and result generation. In this framework, the multimodal large language model acts as the cognitive and reasoning core, while multiple specialized agents are configured to support different inspection requirements. These agents are responsible for interpreting inspection instructions, planning task chains, coordinating external tools, and generating structured evaluation outputs. By connecting remote sensing data, field information, professional models, geospatial analysis tools, and construction-standard knowledge through a unified protocol, the framework provides a flexible technical architecture for handling heterogeneous data and task-specific evaluation procedures. To examine the applicability of the proposed framework, this study further analyzes its adaptation to three typical business scenarios: Completion Acceptance, Construction Effectiveness, and Disaster Impact Evaluation. Although these scenarios differ in input data, evaluation indicators, and tool configurations, they share a common workflow consisting of data access, task planning, tool-chain execution, and evaluation-result generation. This design enables methodological consistency while allowing scenario-oriented customization. For case-based validation, a road accessibility evaluation task under the Completion Acceptance scenario is selected as a representative application. The case focuses on whether the proposed framework can complete a continuous workflow from natural-language task input to evaluation report generation. The case results show that the framework can support the process-oriented execution of a typical inspection and evaluation task for well-facilitated farmland. It is able to parse the inspection request, decompose the task into executable subtasks, invoke relevant data and analysis tools, conduct road-network and plot-accessibility analysis, and generate evaluation results. In addition, the system output is compared with expert-interpreted reference results to examine the consistency of the evaluation. The results indicate that the proposed framework demonstrates preliminary feasibility in task organization, automated processing, and result generation under a representative application scenario. This suggests that the integration of multimodal large language models and collaborative agents can provide a new technical pathway for transforming well-facilitated farmland inspection from manual experience-driven operations toward intelligent and workflow-driven evaluation. It should be noted that this study mainly focuses on the design of a conceptual technical framework and its case-based validation in a typical local task, rather than a fully deployed engineering system. Therefore, further studies are still required to test the framework across more regions, more farmland types, and more complex inspection scenarios. Overall, this study provides a framework-level reference for the intelligent supervision of well-facilitated farmland and offers a basis for subsequent engineering implementation and multi-scenario extension.