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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful context beyond that: it specifies the data source (DOL LCA public data), the type of data returned, and includes an illustrative example with concrete numbers. This enriches the behavioral understanding without contradicting annotations.

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 reasonably concise and front-loaded with the usage guidance, followed by return details and an example. The example is slightly long but illustrative. No redundant filler; each sentence adds value.

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 tool with one required parameter, read-only annotations, and no output schema, the description provides sufficient context: what data is returned, the source, and a realistic example. It is complete enough for an agent to select and invoke the tool correctly. Minor gap: no mention of possible limits or pagination, but not necessary 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?

The input schema already covers the parameter fully with examples ('e.g. Google, Deloitte, Cognizant'), achieving 100% schema description coverage. The description adds minimal extra meaning beyond repeating the employer name in the example; it does not provide format or additional constraints, so the baseline of 3 is appropriate.

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 returns prevailing wage statistics, certified job titles, wage levels, and state distribution from DOL LCA filings, with a specific use case (analyzing employer H-1B compensation strategy). This distinguishes it from sibling tools like get_salary_benchmark or get_company_salary_disclosure by focusing on H-1B/DOL data.

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?

Provides explicit guidance on when to use: 'Use when analyzing an employer H-1B compensation strategy or benchmarking tech sector wages against DOL prevailing wage data.' It does not name alternatives or exclusions, but the context is clear enough to guide selection among siblings.

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