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Scored across 44 tools
Most tools target a distinct resource and action (e.g., create_dataset, get_workflow, start_training_workflow). A few listing tools overlap in scope, such as list_available_actions versus get_worker_set_actions, and list_worker_groups versus list_worker_sets, but descriptions generally clarify the differences.
Nearly all tools follow a consistent snake_case verb_noun pattern. Minor deviations include using obtain instead of get for extraction tools and upload_model instead of create_model, but the overall convention remains predictable.
With 44 tools, the surface is heavy for the apparent synthetic-data platform scope. While the domain is broad, many operations could likely be consolidated or grouped, and 25+ tools is generally too many for an agent to navigate efficiently.
Core synthetic data lifecycle is well covered: dataset CRUD, SQL query, model upload, training config, training workflow, generation workflow, and evaluation plots. Minor gaps exist, such as missing delete_project, delete_workflow, and update_model, but agents can work around these.