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H1b

by io.github.pipeworx-io
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H1b Employer Sponsorship

h1b_employer_sponsorship
Read-onlyIdempotent

Check whether a US employer sponsors H-1B visas and profile their sponsorship, from DOL Labor Condition Application (LCA) disclosures. Answers 'does company X sponsor H-1B / green cards' for recruiting and candidate advising. Returns the number of certified LCA filings, base-salary range (min / median / max), the top sponsored job titles, and top work locations for the employer. Filter by year (DOL federal fiscal year, Oct-Sep, for recent years); defaults to the latest full year. Employer name is matched as the disclosed legal name (e.g. 'Google', 'Amazon.com Services').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFiling year (e.g. 2024). Defaults to the most recent full year. Interpreted as DOL federal fiscal year (Oct-Sep) for recent years.
employerYesEmployer name to look up, e.g. "Google", "Deloitte", "Amazon".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / year / description
      Previous value: -"Filing year (e.g. 2024). Defaults to the most recent full year."New value: +"Filing year (e.g. 2024). Defaults to the most recent full year. Interpreted as DOL federal fiscal year (Oct-Sep) for recent years."
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "employer": "Google",
+    "year": 2024
+  },
+  {
+    "employer": "Amazon.com Services"
+  }
+]
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds genuinely useful context beyond that: the underlying data source, the default-to-latest-full-year behavior, fiscal-year interpretation, and the fact that the employer is matched against the disclosed legal name.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but front-loaded: purpose first, then the question it answers, then the return contents, then filtering semantics. Every sentence carries information, though the return list and year semantics could be tightened slightly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema present, the description compensates by enumerating what is returned (certified LCA count, base-salary min/median/max, top titles, top locations) and explaining the year filter/default. An agent has everything needed to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description goes slightly beyond the schema by clarifying that matching uses the disclosed legal name (e.g. 'Amazon.com Services'), which warns the agent off loose partial names the schema examples ('Amazon') might encourage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Check whether a US employer sponsors H-1B visas and profile their sponsorship') and names the data source (DOL LCA disclosures). It is cleanly distinguishable from siblings like h1b_salary and h1b_top_sponsors, which cover narrower salary/top-sponsor questions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear use case ('Answers does company X sponsor H-1B / green cards for recruiting and candidate advising') and implies the question it is meant to answer. It does not explicitly route to alternatives such as h1b_salary or h1b_top_sponsors when those are more appropriate.

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

All H1b toolsInspect
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