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H1b

by io.github.pipeworx-io

H1b Employer Sponsorship

h1b_employer_sponsorship
Read-onlyIdempotent

Check whether a US employer sponsors H-1B visas and profile their sponsorship, from DOL Labor Condition Application (LCA) disclosures. Answers 'does company X sponsor H-1B / green cards' for recruiting and candidate advising. Returns the number of certified LCA filings, base-salary range (min / median / max), the top sponsored job titles, and top work locations for the employer. Filter by year (defaults to the latest full year). Employer name is matched as the disclosed legal name (e.g. 'Google', 'Amazon.com Services').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFiling year (e.g. 2024). Defaults to the most recent full year.
employerYesEmployer name to look up, e.g. "Google", "Deloitte", "Amazon".

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds behavioral context: returns certified filings count, salary range, top job titles, and locations; explains legal name matching and default year behavior. No contradiction.

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?

The description is a single paragraph with no wasted words. It front-loads the main purpose and then details returned data and parameters. Could be slightly more structured (e.g., list), but it's efficient and clear.

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 lacking an output schema, the description comprehensively covers inputs (employer, optional year), behavior (matching, default year), and outputs (count, salary range, top jobs, locations). No missing information for effective usage.

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%, but the description adds value by explaining the default for 'year' (latest full year) and the matching logic for 'employer' (disclosed legal name). Examples further clarify usage. Baseline 3 plus extra context justifies 4.

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 verb 'Check' and specific resource 'H-1B visa sponsorship' using DOL LCA data. It distinguishes from sibling tools like h1b_salary (salary focus) and h1b_top_sponsors (aggregate view) by emphasizing individual employer profiling.

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 the use case: 'Answers "does company X sponsor H-1B / green cards" for recruiting and candidate advising.' It mentions employer name matching and year filtering but does not explicitly exclude alternative tools or specify when not to use.

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

A3.7/5.0
Disambiguation2/5

Several tool families have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,743 tools with only subtle differences in grounding/fan-out, and the six polymarket_* tools overlap heavily in purpose. Even with detailed descriptions, an agent would frequently need to read the full text to pick the right one, and the beta variant is admitted to be currently identical to the stable router.

Naming Consistency4/5

All tool names use snake_case with clear family prefixes (ask_pipeworx, polymarket_*, h1b_*, pipeworx_*, subscribe/unsubscribe), making the set look organized. The minor inconsistency is that some names start with an imperative verb (ask, compare, validate, scan) while others are bare nouns (entity_profile, deep_research, bet_research), so the verb_noun pattern is not universal.

Tool Count2/5

34 tools is well beyond the recommended range for an MCP server, and many are near-duplicates (three ask_pipeworx variants, six prediction-market analyzers, three memory/three subscription tools). The breadth may reflect a genuinely large data catalog, but exposing it all as top-level MCP tools makes the surface heavy and hard to navigate.

Completeness3/5

For the actual data-platform scope, coverage is solid: lookups, research, memory, subscriptions, and validation are all present. However, a direct fetch tool for the advertised pipeworx:// citation URIs is missing (deep_research even conditions citations on resources/read existing), and the server's stated H-1B identity is underrepresented with only three specialized tools.

Resources