science.molar_mass

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

Annotations already declare readOnly/idempotent/closed-world, so the lower bar applies; the description still adds genuinely non-obvious behavior: IUPAC 2021 conventional weights summed as Decimal with no rounding, plus the precise error boundary. It does not describe response shape, but an output schema exists to cover that.

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?

Four sentences, front-loaded with purpose and return values, then syntax support, then numeric convention, then error limits. No redundant restatement of the name or schema.

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 one-parameter, annotated, output-schema-backed tool, the description covers everything an agent needs: input grammar, numeric precision convention, return composition, and failure modes. Nothing material is missing.

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 coverage is 0% for the single `formula` parameter, so the description must compensate — and it does, specifying case sensitivity, parentheses like Ca(OH)2, hydrate dot notation CuSO4.5H2O, and what is rejected. This is exactly the grammar an agent needs to construct a valid input.

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?

States a specific verb and resource ('화학식으로 몰질량(g/mol)을 계산') and also names the secondary output ('원소별 원자 수(composition)'), so the agent knows both what it computes and what it returns. This is distinguishable from its closest sibling, science.stoichiometry, which is not a mass calculator.

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?

Gives clear preconditions for a valid call — case-sensitive symbols, supported bracket/hydrate notation, the H~Pb 27-element support envelope, and explicit error cases (other elements, ionic charge notation). It does not explicitly route to alternatives, but it clearly delimits when this tool applies versus failing.

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