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

Annotations already indicate readOnlyHint=true and destructiveHint=false, and the description does not contradict them. It adds valuable context about the data source (DOL Office of Foreign Labor Certification), the aggregate nature of the results, and provides a concrete example with wage levels and certified positions. This goes beyond the annotations without introducing ambiguity.

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 use case, followed by a clear output statement, a concrete example, and a source attribution. Every sentence adds value and there is no fluff or repetition of schema details.

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?

Given the absence of an output schema, the description adequately explains what the tool returns (aggregates, example values, source). It could be more explicit about handling optional parameters or return field structure, but the example and dimension list are sufficient for a read-only, filterable data lookup tool.

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 references all four dimensions ('by employer, job title, state, and fiscal year') which map to the schema properties (company_name, job_title, state, fiscal_year), and the example clarifies employer/job title. However, it does not explain value formats, optionality, or parameter combinations, leaving room for interpretation.

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: 'Returns DOL LCA and H-1B wage aggregates by employer, job title, state, and fiscal year.' It includes a specific use case ('Use when benchmarking compensation against disclosed employer wages') and an example, making it easy to distinguish from sibling tools like get_salary_benchmark or get_employer_h1b_wages.

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 first sentence provides explicit use-case context: 'Use when benchmarking compensation against disclosed employer wages or assessing H-1B wage practices before a talent acquisition or competitive hire.' It does not explicitly name alternative tools or exclusion criteria, but the use case is clear enough to guide tool selection.

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