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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral context beyond that: the cost ($0.02 per call), the data source (DOL Office of Foreign Labor Certification), and that it returns aggregates. No contradiction is present. It stops short of mentioning rate limits or request quotas, but for a read-only tool this is solid.

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 four sentences, each earning its place: use case, return summary, illustrative example, and source/cost. The example is somewhat verbose but highly illustrative. It is front-loaded with the trigger scenario and the core return statement.

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 tool's moderate complexity (4 params, no output schema, read-only annotations), the description provides purpose, usage, a concrete output example, data source, and cost. It does not describe the exact response structure, but the example compensates. An agent can confidently decide when and how to invoke it.

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 carries the entire burden of explaining parameters. It explicitly lists the grouping dimensions: employer, job title, state, and fiscal year, which maps directly to the four parameters. The concrete example (Microsoft, software engineer, Washington/California, 2023) illustrates expected values. It doesn't specify exact string formats (e.g., state abbreviation), but it adds substantial meaning to the bare schema.

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 verb and resource: 'Returns DOL LCA and H-1B wage aggregates by employer, job title, state, and fiscal year.' It also opens with a clear use case ('benchmarking compensation against disclosed employer wages'). It doesn't explicitly distinguish from the highly similar sibling get_employer_h1b_wages, but the purpose is unambiguous and concrete.

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 explicit 'Use when' triggers: benchmarking compensation or assessing H-1B wage practices before a talent acquisition. This is clear context for when to call the tool. It does not offer an explicit exclusion or name alternatives, which keeps it just short of a 5.

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