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H1b

by io.github.pipeworx-io

H1b Salary

h1b_salary
Read-onlyIdempotent

Look up real H-1B base salaries for a job title from DOL LCA disclosures — a market wage benchmark backed by actual filed salaries (not estimates). Answers 'what do H-1B software engineers earn at company X / in city Y'. Filter by job title, and optionally by employer, city, and year. Returns salary statistics (count, min / median / average / max) plus a sample of individual records (employer, title, salary, location, dates).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoOptional work city, e.g. "SEATTLE" or "NEW YORK".
yearNoFiling year (e.g. 2024). 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.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations (readOnlyHint, idempotentHint, etc.) already convey safety; the description adds valuable context: data is from actual filings, not estimates, and returns statistics plus sample records.

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 concise and front-loaded, covering purpose, data source, filtering, and output in a few clear sentences without excess.

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?

Despite no output schema, the description fully explains the return value (salary statistics + sample records), making the tool's output predictable.

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 baseline is 3. The description adds extra meaning: job_title matched as substring, year defaults to most recent, limit default 15 max 50. This goes beyond the schema.

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?

The description clearly states the tool looks up real H-1B base salaries from DOL LCA disclosures, distinguishing it from sibling tools like h1b_employer_sponsorship and h1b_top_sponsors.

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?

The description explicitly answers what the tool does and provides filtering options (job title required, optional employer/city/year). It implicitly shows when to use it but lacks explicit 'when not to use' or alternatives.

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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TDQS

A4.1/5.0
Disambiguation3/5

Several overlapping clusters exist: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all handle research questions, polymarket_edges and polymarket_arbitrage both scan for trading opportunities, and discover_tools/suggest_questions serve similar discovery purposes. The long, use-case-specific descriptions help, but an agent could still easily pick the wrong tool among these near-duplicates.

Naming Consistency4/5

Most tools follow a snake_case verb_noun or noun pattern (resolve_entity, validate_claim, list_subscriptions, h1b_salary), which is fairly consistent. However, there are deviations: bare verbs like recall/remember/forget/subscribe, noun-only phrases like entity_profile and recent_changes, and the ask_pipeworx_beta suffix variant break the pattern slightly.

Tool Count2/5

34 tools is well past the 25+ threshold, and the server is named 'H1b' while only 3 of the 34 tools relate to H-1B data. The rest is a sprawling mix of data research, prediction-market analytics, memory, subscriptions, AI visibility checks, and unrelated utilities like generate_llms_txt and scan_dependency — a severe scope mismatch.

Completeness4/5

As a de facto Pipeworx research platform, the surface is nearly complete: open-ended queries, grounded evidence mode, deep multi-source research, entity resolution, comparison, claim validation, subscriptions, memory, and feedback. The H-1B sub-domain covers employer, salary, and top-sponsor lookups, though the mention of green cards is a small mismatch since only LCA data is provided.

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