Large Language Model (LLM)-based agent systems are increasingly used for scientific tasks, yet their practical capability remains constrained by the narrow scope of manually curated tools they can invoke. Much scientific computational functionality already exists in open-source code repositories, but these resources remain difficult to standardize, operationalize, and invoke reliably for agent use. Here we present ToolRosella, a framework that automatically transforms heterogeneous scientific code repositories into standardized, agent-invocable tools. ToolRosella combines repository analysis, tool interface construction, execution testing, and iterative repair to address the problem of repository-to-tool standardization. Across 122 GitHub repositories spanning 35 subdisciplines in six domains, ToolRosella reaches a 61.5% repository conversion success rate after iterative repair, with a 4.4 speedup over human engineers. The resulting 1,580 callable tools support a downstream task success rate of 84.0% and improve performance when integrated into other agent frameworks, particularly on tasks whose required tools are absent from fixed, curated inventories.
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Figure 1: Overview of ToolRosella: a , ToolRosella’s strength lies in how to automatically transform heterogeneous scientific resources into callable tools, bridging existing repositories and agent execution. Unlike fixed, manually curated tool libraries, it focuses on repository-to-tool standardization for reusable tool construction. b , The pipeline for using ToolRosella to complete scientific tasks.
Figure 2: Overview of the ToolRosella ecosystem. Each node represents a GitHub repository automatically converted into standardized tool services, organized into five major scientific areas (Physical Sciences, Earth & Environmental Sciences, Biological Sciences, Health Sciences, and Scientific Community & Society) and Computer Science. Node groupings reflect sub-domain categorization within each area.
Figure 3: Automated tool conversion performance evaluation. a , Radial bar chart of average completion time per subdiscipline (in minutes) for ToolRosella, human experts, and the LLM-only baseline. b , Repository conversion success rates across 35 subdisciplines within six domains, comparing all three methods. c , Joint visualization of success rate versus completion time, where each point represents one subdiscipline. d , Repair-focused ablation showing cumulative improvement from successive rounds of the Review-Revise-Fix (RRF) mechanism across six domains. e , Error types and their resolution statistics after three RRF rounds.
Figure 4: Downstream task evaluation results. a , Execution success rate and verified success rate of ToolRosella and five baseline systems (SciToolAgent, ChemCrow, RepoMaster, OpenAgents, and OpenClaw) across the six domains. b , Verified task completion success rates across 35 subdisciplines spanning the six categories. Stars denote 23 out-of-distribution (OOD) subdisciplines that are not covered by curated baseline tool sets. c , Verified performance gains when integrating ToolRosella-converted tools into three existing agent frameworks (OpenAgents, RepoMaster, and OpenClaw), with percentage annotations indicating improvement per domain.
Figure 5: Multi-domain case studies of ToolRosella for scientific workflow assistance. The framework converts user queries into executable MCP-compatible toolchains, enabling coordinated orchestration of task-specific analytical modules. Case 1 illustrates a stroke-analysis workflow integrating unsupervised, predictive, and causal inference tools, together with feature-importance summaries and treatment-effect estimates. Case 2 illustrates sequence-analysis workflows combining validation, compositional analysis, and database-driven species prediction. Case 3 illustrates a perovskite material design-and-evaluation workflow spanning literature mining, candidate generation, virtual screening, and mechanistic interpretation.
Figure 6: Vulnerable vs. Secure workflow, illustrated with representative cases of (i) data exfiltration in biomedical pipelines, (ii) command injection in legacy tool invocation, and (iii) resource exhaustion in High-Performance Computing (HPC) environments.