Check the source datasets, coverage dates, and caveats behind H-1B sponsorship answers to verify data provenance and context.
Which data the answers come from, the dates covered, and the caveats.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds that the response includes caveats and date coverage, which is meaningful disclosure about content, but it does not clarify whether the info is global to the dataset or specific to a prior query.
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?
A single compact clause with no filler, and the three returned elements are front-loaded. It loses a point only for being a sentence fragment without a leading verb, which slightly blunts immediate parsing.
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?
An output schema exists and there are no parameters, so the description carries a light burden, and it names the three content areas returned. However, it never states that the tool takes no arguments or that it describes the whole dataset rather than a specific result, leaving a small interpretive gap.
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 tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies; an empty schema leaves no semantic gap.
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 conveys the content returned (data provenance, date coverage, caveats) but is a noun-phrase fragment with no verb, so the action (retrieve metadata about the dataset) must be inferred. It gives no differentiation from siblings like company_sponsorship or salary_range, which is the main gap.
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?
There is no explicit when-to-use guidance, no mention of prerequisites, and no reference to any sibling tool. An agent must guess that this is the metadata/provenance call to make before or alongside the data-returning siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.