Identify a lab marker from its label

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

With no annotations provided, the description is the sole source of behavioral disclosure. It clearly states the main behavior (resolve label, report units) and adds specifics like handling abbreviations and non-English names. However, it does not disclose potential edge cases such as ambiguous labels, return format, or error behavior, which leaves some uncertainty for an agent.

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 two sentences, front-loaded with the core action ('Resolve a lab-report label...'), and every clause adds value. There is no repetition of schema information, and the structure is clear and scannable.

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 (1 param, no output schema), and the description covers the main purpose and a use case. It does not explicitly describe the return shape beyond mentioning units, but for an identification tool this is largely adequate. With no output schema, a bit more detail on the return value would improve completeness, but it remains sufficient for basic 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?

The single parameter 'label' is already described in the schema with examples. The description adds semantic value by clarifying that the label can be an abbreviation or non-English name and is a 'lab-report label', reinforcing the local-naming normalization use case. This goes beyond the schema's basic example list.

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's function: resolving a lab-report label (including abbreviations and non-English names) to a canonical analyte and reporting its possible units. This distinguishes it from the sibling tools convert_lab_value (which converts values) and list_analytes (which lists analytes), making the purpose unambiguous.

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?

The description provides clear context on when to use the tool ('useful for normalising results parsed from labs that use local naming conventions'), which implies the appropriate scenario. It does not explicitly name alternatives or exclusions, but the sibling tool names and the described purpose implicitly differentiate it from conversion and listing tools.

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