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Scored across 12 tools
Each tool targets a distinct operation: 2D/3D/batch segmentation are clearly separated by dimension and scope, restoration tools (denoise, deblur, upsample) use distinct model types, and utilities like save_masks and load_image_info are independent. There is no overlapping purpose that would cause an agent to misselect.
All tool names follow consistent snake_case with a verb_noun pattern (segment_cells_2d, denoise_image, train_segmentation_model, etc.). Naming is uniform and predictable, with clear singular verbs and no mixing of conventions.
12 tools is a well-scoped set for a cell analysis server, covering segmentation, restoration, training, model listing, estimation, and utility functions. Each tool serves a clear purpose and the count is within the ideal 3-15 range.
The surface covers core workflows (segment, restore, train, estimate, save, inspect), but there is a notable gap: after training a custom model, there is no tool to apply that model for segmentation—the segmentation tools only accept pretrained model types. This creates a dead-end in the lifecycle and limits practical use.
