calculate_selectivity

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

With no annotations, the description carries the behavioral disclosure burden. It explains the mathematical definition, unit cancellation, and interpretation of returned values (S_AB > 1, < 1, = 1). It omits edge-case handling but adds meaningful behavioral detail.

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?

The description is compact and well-organized: purpose, formula, parameter details, and return interpretation. Every sentence earns its place with no redundancy.

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?

For a simple calculation tool without an output schema, the description sufficiently covers formula, input sources, and output semantics. It lacks error-handling details and optional-label explanation, but is complete enough for typical invocation.

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%, so the description must compensate. It explains loading_a/b and feed_fraction_a/b with units and constraints, but does not explain label_a/b. Since labels are optional and self-evident from defaults, this is a minor gap.

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 clearly states the tool calculates adsorption selectivity S_AB from mixture GCMC results, supported by the exact formula. This specific verb+resource+scope distinguishes it from sibling parsing/plotting tools.

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?

It gives clear context by specifying the input source (parse_raspa_output()['components'] for GCMCMixture) and notes feed fraction constraints. It does not explicitly list alternatives or exclusions, but the intended usage is unambiguous.

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