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H1b

by io.github.pipeworx-io

H1b Top Sponsors

h1b_top_sponsors
Read-onlyIdempotent

Find which US employers sponsor the most H-1B visas for a given job title (optionally in a specific city) — a candidate-sourcing / target-account signal for recruiting. Answers 'which companies sponsor the most data engineers in Austin' or 'top H-1B sponsors for nurses'. Returns employers ranked by certified LCA filings for the role, each with their filing count and median base salary. Backed by real DOL LCA disclosures.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoOptional work city to scope to, e.g. "AUSTIN" or "NEW YORK".
yearNoFiling year (e.g. 2024). Defaults to the most recent full year.
limitNoMax employers to return (default 15, max 50).
job_titleYesJob title to rank sponsors for, e.g. "data engineer", "physical therapist".

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already cover safety traits (readOnly, idempotent). Description adds data source ('Backed by real DOL LCA disclosures') and clarifies returns, but doesn't add major new behavioral details beyond what annotations provide.

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?

Three sentences, front-loaded with purpose, no waste. Each sentence adds value.

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?

Returns are well described (employers ranked by certified LCA filings, with filing count and median base salary). No output schema but explanation is sufficient for selection.

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% with each parameter described. Description examples illustrate usage (e.g., uppercase city, limit max) but add minimal value beyond 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?

Description uses specific verbs ('Find which US employers sponsor'), names the resource ('top H-1B sponsors'), and gives clear query examples that distinguish it from siblings like 'h1b_employer_sponsorship' and 'h1b_salary'.

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?

Explicitly states usage for 'candidate-sourcing / target-account signal for recruiting' and provides example queries. No explicit when-not-to-use, but sibling differentiation is clear from context.

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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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.

Resources