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janishkumar

h1b-sponsor-mcp

by janishkumar

salary_range

Read-only

Get H-1B offered wage ranges for a role—including percentiles and experience-level pay—over the last 12 months, with optional state, city, or company filters.

Instructions

Offered H-1B wages for a role (percentiles and by experience level), last 12 months.

Args:
    role: Job, e.g. "data scientist", "software engineer".
    state: Optional two-letter worksite state.
    city: Optional worksite city.
    company: Optional employer to restrict to.

Input Schema

NameRequiredDescriptionDefault
cityNo
roleYes
stateNo
companyNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish that this is a read-only, closed-world lookup, so the bar is lower. The description still adds meaningful dataset context beyond annotations: the wages are 'offered H-1B wages' rather than general salaries, the data covers 'last 12 months,' and results include percentiles and experience-level breakdowns.

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

Conciseness5/5

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

The description is front-loaded with the core purpose and then gives a compact, well-structured parameter list. Every sentence and argument line earns its place without unnecessary preamble.

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

Completeness4/5

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

An output schema exists, so return-value structure need not be explained. The description covers dataset scope, timeframe, and all parameters adequately. Minor gaps remain around data-source limitations and how filters combine, but an agent has enough information to invoke the tool 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 description coverage is 0%, so the description carries the burden of explaining parameters. It defines all four: role with examples, state as a two-letter worksite state, city as worksite city, and company as an employer restriction. It could go slightly further on interactions such as state plus city, but the semantics are largely covered.

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

Purpose4/5

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

The description states a specific resource and scope: offered H-1B wages for a role, with percentiles, experience-level breakdowns, and a 12-month window. This is clearly distinct from sibling tools like company_sponsorship or top_sponsors, though it does not explicitly name or contrast those alternatives.

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

Usage Guidelines3/5

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

Usage is only implied by the description and optional parameter labels. There is no explicit guidance on when to choose this tool over siblings such as compare_companies or company_sponsorship, nor are there exclusions or prerequisites.

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