thermophysical-curator
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| inspect_moleculeC | Validate and canonicalize a SMILES string and report model-domain flags. |
| predict_jrmpnnB | Predict Tm, Tb, Tc, Pc, and/or Vc with the supplied JR-MPNN checkpoints. Property symbols are Tm, Tb, Tc, Pc, and Vc. Returned intervals are clearly marked as uncalibrated because the paper's k-NN calibration artifacts are not present in this workspace. |
| assess_jrmpnn_training_similarityB | Assess similarity to a property's private JR-MPNN training embeddings. Uses mean Euclidean distance to the 10 nearest graph embeddings. Private Training SMILES, embeddings, and reference identifiers are never returned. |
| estimate_umansyspropC | Run UManSysProp group-contribution estimates without its legacy web UI. |
| curate_thermophysical_recordsA | Normalize, deduplicate, score, and triage records with a full audit trail. Each record should contain smiles, property, value, and unit. Strongly recommended provenance fields are source or doi, experimental, method, and uncertainty. The tool never silently removes a record. |
| server_capabilitiesB | Describe supported properties, methods, and important scientific limits. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
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.
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.
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.
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.