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by io.github.pipeworx-io
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Perm Employer Certifications

perm_employer_certifications
Read-onlyIdempotent

Check whether a US employer sponsors green cards (PERM labor certification) and profile their sponsorship, from DOL PERM (Form ETA-9089) disclosures. Answers 'does company X sponsor green cards' for recruiting and candidate advising. Returns the number of certified PERM filings, offered-wage range (min / median / max), the top sponsored job titles, and top worksite locations for the employer. Filter by year (DOL federal fiscal year, Oct-Sep); defaults to the latest full year. Employer name is matched as the disclosed legal name (e.g. 'Microsoft Corporation', 'Amazon.com Services LLC').

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "year": 2025,
      "employer": "Microsoft"
    },
    "name": "perm_employer_certifications"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNoFiling year (e.g. 2025). Defaults to the most recent full year this pack has data for. Interpreted as DOL federal fiscal year (Oct-Sep).
employerYesEmployer name to look up, e.g. "Microsoft", "Oracle", "Amazon".

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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 the safety bar is lower; the description still adds real substance by naming the data source (DOL PERM Form ETA-9089 disclosures), the fiscal-year semantics with Oct–Sep boundaries and latest-full-year default, and the legal-name matching caveat. It does not mention update cadence or data-lag caveats, which keeps it from a 5.

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?

Four sentences, front-loaded with the core question and answer, followed by return shape and then filtering/naming guidance. Dense but every sentence carries information; the fiscal-year sentence duplicates the schema somewhat, a minor redundancy.

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?

No output schema exists, and the description compensates by enumerating the return fields (certified filing count, min/median/max offered wage, top titles, top worksites). Combined with the year default and name-matching rule, an agent has everything needed to call it correctly and interpret the result.

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 beyond by warning that 'employer' must match the disclosed legal name (e.g. 'Microsoft Corporation', 'Amazon.com Services LLC'), which is a real failure mode the schema's loose examples ('Microsoft', 'Oracle') do not convey. The year/fiscal-year detail largely repeats the schema, so it is not a full 5.

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: checks whether an employer sponsors green cards via DOL PERM filings, and explicitly names the user question it answers ('does company X sponsor green cards'). The entity-level sponsorship profile is distinct from the sibling tools perm_salary and perm_top_sponsors, which operate on wage and aggregate-sponsor cuts respectively.

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 clear usage context ('for recruiting and candidate advising') and the framing question it answers, which tells the agent when to reach for it. It stops short of naming alternatives or exclusions (e.g. when to prefer perm_salary or perm_top_sponsors), so it lands above generic but below fully prescriptive.

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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