PolarTools: Benchmarking Tool-Augmented Multimodal Agents for Polar Remote Sensing Analysis

Recent advances in multimodal large language models (MLLMs) and autonomous agents have opened up new opportunities for automated scientific analysis. However, their capabilities in specialized domains such as polar remote sensing remain largely unexplored. Existing polar benchmarks primarily evaluate perception models, while it remains unclear whether domain-specific tools provide additional value beyond the growing code-generation capabilities of MLLMs. To address this gap, we introduce PolarTools, a benchmark for evaluating tool-augmented multimodal agents in polar remote sensing. PolarTools contains 710 question-answer instances covering iceberg, sea-ice, and polar oil-spill analysis, spanning perception and evidence interpretation; object and spatial structure analysis; temporal change and motion; geometric measurement and counting; quantitative product diagnostics; and preprocessing and cartography. We further develop a unified environment integrating 113 domain-specific tools for preprocessing, target extraction, geospatial analysis, temporal modeling, and visualization. Using PolarTools, we design two evaluation modes: General Solving (GS), in which agents are provided with a Python environment in a Docker container and rely on their own capabilities to solve tasks, and Tool-Augmented Solving (TA), in which agents additionally have access to polar-specific tools. We conduct experiments on 13 multimodal model configurations to analyze how domain-specific tools affect task completion, execution behavior, and efficiency. Unlike trajectory-matching evaluations, PolarTools evaluates agents based on task requirements and verifiable outputs, allowing diverse valid solutions while enabling analysis of whether domain-specific tools improve task completion and efficiency. PolarTools provides a testbed for studying multimodal agents in polar scientific workflows and the value of domain-specific tool augmentation.