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Perm

by io.github.pipeworx-io
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Perm Salary

perm_salary
Read-onlyIdempotent

Look up real green-card (PERM) offered wages for a job title from DOL PERM disclosures — a market wage benchmark backed by actual certified filings (not estimates). Answers 'what do green-card-sponsored software engineers earn at company X / in city Y'. Filter by job title, and optionally by employer, city, and year. Returns wage statistics (count, min / median / average / max) plus a sample of individual records (employer, title, wage, location, dates).

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,
      "job_title": "data engineer"
    },
    "name": "perm_salary"
  }
}

Input Schema

NameRequiredDescriptionDefault
cityNoOptional worksite city, e.g. "SEATTLE" or "AUSTIN".
yearNoFiling year (e.g. 2025). Defaults to the most recent full year.
limitNoMax sample records to return (default 15, max 50).
employerNoOptional employer name to scope to, e.g. "Meta".
job_titleYesJob title to search, e.g. "software engineer", "data scientist". Matched as a substring of the disclosed title.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint, so the safety profile is covered. The description adds meaningful context beyond that: the data source is actual certified DOL PERM filings rather than estimates, and it discloses the return shape (aggregate stats plus a sample of individual records with employer, title, wage, location, dates). It doesn't mention data freshness/lag or coverage limitations of the disclosure dataset, which would have pushed it higher.

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?

Front-loaded with the core purpose in the first clause, then the motivating question, then filters and return format — a sensible order. Slightly long, and the return-shape sentence could be tightened, but every sentence carries information an agent needs.

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, the description steps up and describes the return payload (count, min/median/average/max, plus sample records with their fields), and it covers the required vs optional filter structure. For a read-only lookup tool with a fully documented schema, nothing essential is missing.

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

Parameters3/5

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

Schema coverage is 100%, so every parameter including limit and year defaults is already documented in the schema. The description restates the filter dimensions (title, employer, city, year) and notes job_title is required, but adds no matching semantics of its own beyond what the schema provides (the substring matching rule lives in the schema, not the description). Baseline 3 is appropriate.

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: lookup of DOL PERM disclosed offered wages for a job title, explicitly framed as a certified-filing benchmark rather than an estimate. The question-shaped framing ('what do green-card-sponsored software engineers earn at company X / in city Y') makes the intended use unambiguous and separates it from siblings like perm_top_sponsors and perm_employer_certifications, which cover sponsors and certifications rather than wages.

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?

Provides clear context on when to reach for it (wage benchmarking for a title, optionally scoped by employer/city/year) and states that job_title is the driver filter with the rest optional. It stops short of naming sibling alternatives such as perm_top_sponsors or perm_employer_certifications or saying when *not* to use this tool, so it lands at 4 rather than 5.

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

All Perm toolsInspect
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