predict_jrmpnn

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full behavioral burden, and it does disclose one real trait: returned intervals are uncalibrated because k-NN calibration artifacts are absent. However, it omits other important behavior — where 'supplied' checkpoints come from, failure modes for invalid SMILES, and whether every requested property is always returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the action and target. It wastes a little space restating the property symbols that the first sentence already named, but overall it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value explanation is not required, and the description does flag the calibration caveat. Still missing are sibling differentiation, checkpoint sourcing, and any parameter-level detail for `smiles`, leaving meaningful gaps for a tool with zero schema coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for two parameters, so the description must compensate. Enumerating the valid property symbols partially documents the `properties` argument, but the required `smiles` parameter is never explained (format, accepted inputs), and the array/default semantics remain unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb ('Predict') and a concrete set of resources (Tm, Tb, Tc, Pc, Vc) tied to a named model (JR-MPNN checkpoints). It is clear what the tool does, but it never distinguishes itself from the sibling estimate_umansysprop, which appears to predict the same class of thermophysical properties.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool rather than estimate_umansysprop, nor any prerequisite (e.g., which checkpoints must exist, whether a workspace setup is needed). 'and/or' hints that the property subset is user-selectable, but that is inference rather than guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.