inspect_molecule

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

No annotations are provided, so the description carries the full behavioral burden. It does not state what happens with an invalid or non-canonicalizable SMILES, whether errors are raised or flagged, or what the flags mean, leaving the mutation/validation behavior largely opaque.

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?

A single front-loaded sentence with no wasted words; the core action and the output signal are both present immediately. It is terse enough to be efficient but leaves several behavior questions unaddressed.

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?

The tool is simple (one input, output schema present) so return values need not be explained, but the description omits error/failure semantics and the meaning of 'model-domain flags', which an agent would need for correct use.

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?

With one parameter at 0% schema description coverage, the description must compensate, and it does identify the input as a SMILES string. However it adds no format constraints, length limits, or examples beyond that basic identification.

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 names specific verbs and a resource: validate and canonicalize a SMILES string, plus report model-domain flags. This is clearly distinguishable from prediction-oriented siblings like predict_jrmpnn and estimate_umansysprop, though it never explicitly names an alternative.

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, when not to, or which sibling to prefer. The 'model-domain flags' phrasing weakly implies a pre-prediction check, but the agent must infer that entirely.

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