wagesSalary data for any job title: employment, mean and median annual and hourly pay, and 10th to 90th percentile annual pay, nationally and for a US state, from the BLS occupational wage survey. Job titles are matched to SOC codes automatically. Costs $0.006 USDC per call (x402).
| Name | Required | Description | Default |
|---|---|---|---|
| soc | No | 6-digit SOC code instead, e.g. 15-1252 | |
| state | No | State name or code | |
| occupation | No | Job title |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only cover readOnlyHint and openWorldHint, so the description carries the rest and does so usefully: it discloses the data source (BLS occupational wage survey), the automatic SOC matching behavior, and a concrete per-call cost ($0.006 USDC via x402). Cost and payment-mechanism disclosure is material behavior an agent needs before invoking, though latency/rate limits and failure modes are absent.
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?
Two sentences, front-loaded with the core value proposition and packed with substantive detail rather than filler. The first sentence is a long enumerated list, which is dense but justified given there is no output schema.
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 compensates well by enumerating returned metrics and geographic granularity, and it covers provenance and cost. It leaves one gap: zero parameters are required, so it is unstated what happens if no selector (occupation, soc, state) is provided.
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 100%, so the baseline is 3, but the description adds real meaning: it clarifies that `occupation` accepts free-form job titles because they are matched to SOC codes automatically, and the 'nationally and for a US state' phrasing signals that `state` is optional and narrows scope. It does not explain precedence when both `soc` and `occupation` are supplied.
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?
States a specific resource (occupational salary data) and enumerates exactly what is returned: employment, mean/median annual and hourly pay, 10th-90th percentile pay, national and by US state. It does not differentiate itself from plausible siblings like `labor`, `labor_state`, or `jobs`, which likely overlap, so it stops short of a 5.
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?
Usage is implied by the scope statement ('Salary data for any job title') and by the note that job titles are auto-matched to SOC codes, which tells the agent it can pass a title rather than a code. However, there is no explicit when-to-use/when-not guidance and no routing to or away from sibling tools such as `labor` or `labor_state`.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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