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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it is a safe read operation. The description adds valuable behavioral context by detailing the return contents (back wages assessed, employees affected) and even provides an example output. It also cites the DOL WHISARD database as the source, which is a useful disclosure.

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 front-loaded with the usage context, then explains the output, and includes a concrete example. It is somewhat lengthy but every sentence adds value: usage guidance, return details, predictive insight, and an illustrative output. The structure is logical, though the example could be shortened.

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?

With no output schema, the description takes on the burden of explaining return values, which it does by listing violation types and metrics (back wages, employees affected) and giving an example. It also covers 'why' to use the tool (predicts class action exposure). Missing details like pagination, ordering, or state filter behavior are minor given the tool's simplicity.

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 for the two parameters (employer_name and state). However, it does not explain either parameter explicitly. The example implies employer_name accepts an employer name, but there is no guidance on what 'state' means, whether matching is fuzzy/exact, or how to format names. This is a clear gap given the low structured coverage.

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 states a specific purpose with a clear verb and resource: 'Returns DOL Wage and Hour Division enforcement history' and lists violation types (FLSA overtime, minimum wage, child labor). It distinguishes itself from sibling compliance tools by focusing on DOL labor violations rather than sanctions, exclusions, or other regulatory domains.

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 explicitly states when to use the tool: 'Use when screening an employer, vendor, or acquisition target for wage and hour compliance risk before a contract award, supply chain partnership, PE acquisition, or HR due diligence review.' It provides clear context but does not explicitly mention when NOT to use it or name alternatives, though the sibling list implies other use cases.

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