A
licenseB
qualityD
maintenancePredict the cost of an LLM call before you make it, and pick the cheapest model that still does the job, offline, from your editor.
7
49 npm
Apache 2.0
P2predict-mcp
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TDQS
A4.3/5.0
Scored across 12 tools
Disambiguation4/5
The prediction tools (predict, predict_batch, predict_from_csv, predict_interval, explain, what_if) have overlapping surface areas but each has a clearly distinct input mode or output focus. The one genuinely close pair is get_model_quality and generate_report; their descriptions do distinguish structured JSON from PDF generation, but an agent could still hesitate.
Naming Consistency4/5
Most tools follow a clear snake_case verb or verb_noun pattern, and the predict_* family is consistently named. Minor deviations like 'explain' and 'what_if' break the pattern slightly but remain readable and predictable.
Tool Count5/5
Twelve tools is well within the ideal range and each tool covers a distinct stage of the model workflow: discovery, training, prediction variants, explanation, quality assessment, and reporting. No tool feels redundant enough to remove.
Completeness4/5
The set covers model discovery, training, single/batch prediction, intervals, what-if analysis, explanation, quality review, and PDF reporting—very complete for a prediction-focused server. The main gaps are missing model deletion/update operations and a direct model-comparison tool, but these are not core to the stated purpose.
Maintenance
ActivityActive
ResponsivenessNo issues