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

Annotations already flag readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: data source (DOL LCA public data), the types of statistics returned (prevailing wage, certified job titles, wage levels, state distribution), and a per-call cost ($0.02 USDC). This goes beyond the annotations and gives the agent a clear idea of what to expect.

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 efficient: use case, return summary, a concrete example, source, and cost. The example is somewhat lengthy but serves a pedagogical purpose. The information is front-loaded with the usage trigger. No wasted sentences, though a slightly tighter phrasing would push it to 5.

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 must explain return values, and it does: prevailing wage statistics, certified job titles, wage levels, and state distribution. The Google example gives a realistic sense of the data shape and magnitude. It also covers source and pricing, making the description complete for a simple single-parameter read-only 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 coverage is 100% and the parameter `employer_name` is already described with examples in the schema. The description only reinforces this with a Google example without adding new constraints, formats, or edge-case semantics. Baseline 3 is appropriate since the schema carries the descriptive load.

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 explicitly states a specific use case ('analyzing an employer H-1B compensation strategy or benchmarking tech sector wages against DOL prevailing wage data') and names the exact resource (DOL LCA filings). It distinguishes itself from siblings like get_salary_benchmark and get_labor_market_benchmark by focusing on employer-specific H-1B wages, making the tool's 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?

It provides clear context with 'Use when...' and gives a concrete example. However, it does not explicitly mention when not to use it or point to alternatives such as get_salary_benchmark for general wage data. Thus it meets the 'clear context' level but stops short of full exclusion/alternative guidance.

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