tensorart-mcp
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TDQS
Scored across 9 tools
Most tools target clearly distinct operations: generation (generate_image, image_to_image), workflow execution (run_workflow, run_workflow_template), and job/model management (get_job, cancel_job, get_model, upload_image, estimate_credits). The only mild overlap is run_workflow vs run_workflow_template, but descriptions distinguish raw node maps from saved templates.
All names use snake_case and mostly follow a verb_noun pattern (get_job, generate_image, estimate_credits, run_workflow, cancel_job, get_model, upload_image). image_to_image is a noun-style domain term that slightly breaks the verb_noun convention, but it's conventional and readable.
Nine tools is well within the ideal 3-15 range for an image-generation service. Each tool covers a distinct capability (generation, workflows, job control, model info, upload) without obvious redundancy.
The surface covers the core lifecycle: generate, img2img, workflows, job status/cancel, credit estimation, model info, and upload. The main gap is model/LoRA discovery—get_model requires a known id and there's no list/search tool—but agents can work around this with ids from the web UI.