A
licenseNot graded
qualityB
maintenanceEnables estimation of PC-SAFT pure-component parameters and prediction of properties like density and vapor pressure for any molecule or mixture using Graph Neural Networks.
GPL 3.0
thermophysical-curator
by frankmtetwa
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TDQS
B3.2/5.0
Scored across 6 tools
Disambiguation4/5
Each tool has a distinct role: SMILES validation, JR-MPNN prediction, training-similarity/applicability-domain assessment, UManSysProp estimation, record curation, and a capabilities meta-tool. The two property-prediction tools (predict_jrmpnn and estimate_umansysprop) overlap in output but are clearly separated by method, so mild confusion is possible but descriptions resolve it.
Naming Consistency4/5
Names are uniformly snake_case with clear verb_noun structure (inspect_molecule, predict_jrmpnn, assess_jrmpnn_training_similarity, estimate_umansysprop, curate_thermophysical_records). The lone noun-only server_capabilities is a minor deviation from the verb-led pattern.
Tool Count5/5
Six tools is well-scoped for a thermophysical prediction-and-curation server, with each tool covering a coherent responsibility. No tool feels redundant or missing at the count level.
Completeness4/5
The surface covers the core lifecycle: molecule validation, two prediction paths, applicability-domain assessment, curation with audit trail, and a capabilities descriptor. Batch prediction/curation or result-export operations are not exposed, but the primary workflows are covered without obvious dead ends.
Maintenance
ActivityMaintained
ResponsivenessNo issues