H-1B Sponsor

io.github.janishkumarPublished by GitHub account janishkumarv0.1.1Updated Nov 5, 2026

Is this company still sponsoring H-1Bs and green cards? Trends from DOL and USCIS filings.

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Overview

AI-generated overview

Answers whether US employers still sponsor H-1B visas and green cards, using public DOL and USCIS filing data in markdown tables.

What you can do
  • Check one employer's sponsorship: certified H-1B LCAs for recent windows versus a year earlier, new employment versus transfers, PERM green-card certifications, USCIS approvals and denial-rate flags, plus a still-sponsoring verdict
  • Compare several employers side by side on filings, trend, green cards and denial rate, optionally filtered by role, state or city
  • Rank top sponsors by certified filings for a role and place over a chosen look-back window
  • Look up offered H-1B wages for a role by percentile and experience level, optionally narrowed to a state, city or employer
  • Resolve a company name to the exact legal employer entities that file separately
  • Explain which data the answers come from…
When to use it
Useful for job seekers or advisors weighing which US employers actually file H-1B and green-card cases, comparing a shortlist of companies, or checking typical offered wages for a role and location before applying.
Requirements
Runs locally over stdio via uvx from the PyPI package, so uv must be installed. The first run downloads a roughly 61 MB compressed database and caches it; afterwards it works offline. No account, API key or environment variable is declared.
Before you install
Answers are public filing data, not legal advice, and cannot confirm that a specific team will sponsor a specific role. LCA counts are an upper bound on sponsorship activity, data lags government publication by roughly 1.5 to 5 months, and USCIS denial rates are company-wide because job titles are not published. Name matching is prefix-based, so unrelated companies can appear; check the listed entities.

Installation

In SourceWeft

  1. Open H-1B Sponsor in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

h1b-sponsor-mcp

[CI]

Ask Claude "is this company still sponsoring H-1Bs?" and get a straight answer in tables, built from 1.28 million public government filings plus USCIS approval records.

An MCP server for job seekers who'll need visa sponsorship. It combines all three public sponsorship signals for each employer, shows how they've moved over the last six months against the same months a year earlier, and runs locally on a database that updates each quarter.

You:     Compare Microsoft, Amazon, Google, Meta, Apple, Nvidia and TCS for data scientist sponsorship.Claude:  (calls compare_companies)
CompanyLCAs Jan–Jun 2026of which new employmentvs a year earlierPERM certified, last 12 moUSCIS new-employment approvals FY2025USCIS denial rate FY2025–FY2026 to Q3Still sponsoring?
Microsoft Corporation351152-15%1471,394🟢 2.6%🟢 Yes, active
Amazon.com Services LLC616334+32%14,637🟢 1.3%🟢 Yes, active
Google LLC240150+8%01,042🟢 1.2%🟢 Yes, active
Meta Platforms, Inc386210+2%91,554🟢 1.3%🟢 Yes, active
Apple Inc.13767+69%3827🟢 1.1%🟢 Yes, active
NVIDIA Corporation70+75%8560🟢 0.7%🟢 Yes, active
TATA CONSULTANCY SERVICES LIMITED10—0846🟡 6.1% Elevated🟡 Occasional

Real output, data through Jun 30, 2026. LCA and PERM columns are filtered to data-scientist roles; the USCIS columns are company-wide.

That one table answers three questions most sponsorship sites can't. Is the company still filing this year, and in which direction? And will it take you through to a green card? Amazon and Google are filing plenty of H-1Bs for data scientists, but certified almost no green-card (PERM) cases for them in the last 12 months. And how often does USCIS turn this employer down? TCS's 6.1% denial rate is more than twice the national 2.5%, so it's flagged.

What it can answer

ToolExample question
company_sponsorship"Is Stripe still sponsoring? Just data roles in New York."
compare_companies"Compare Databricks, Snowflake and Stripe."
top_sponsors"Who sponsors machine learning engineers in Georgia?"
salary_range"What do H-1B data scientists get offered in Atlanta, by level?"
find_employer"Which Deloitte entities file separately?"
data_info"What data is this based on, and how current is it?"

Every answer is a markdown table that names its date window ("Jan–Jun 2026", never just "last 6 months") and says what the numbers do and don't mean.

Every verdict and flag starts with a signal, so a table reads at a glance: 🟢 good · 🟡 caution · 🔴 warning · ⚪ not enough data. The word after the signal says the same thing, for anyone reading without emoji.

Install

You need uv. The package is on PyPI. The first run downloads the database (about 61 MB compressed) and caches it; after that everything is local and offline.

Claude Desktop: add to claude_desktop_config.json:

json
{  "mcpServers": {    "h1b-sponsor": {      "command": "uvx",      "args": ["h1b-sponsor-mcp"]    }  }}

Claude Code:

bash
claude mcp add h1b-sponsor -- uvx h1b-sponsor-mcp

Any other MCP client works the same way: run uvx h1b-sponsor-mcp over stdio. To run the newest code from GitHub instead of the PyPI release, use uvx --from git+https://github.com/janishkumar/h1b-sponsor-mcp h1b-sponsor-mcp.

Where the numbers come from

SignalSourceWhat it tells youCoverage
H-1B LCAsDOL Labor Condition Application disclosuresThe freshest signal: an employer is preparing H-1B petitions. Split into new employment (e.g. from F-1/OPT) and transfers from other employers.Decisions Oct 2024 – Jun 2026 (1.02M filings)
Green cardsDOL PERM disclosuresWhether the company sponsors permanent residenceDecisions Oct 2024 – Jun 2026 (259K cases)
Confirmed petitionsUSCIS H-1B Employer Data HubApprovals and denials by petition type, per fiscal yearFY2023 – FY2026 Q3

How current is it? DOL publishes each quarter about 5–6 weeks after it ends, so the data is between 1.5 and 5 months old at any time. Every H-1B site uses these same files, so none can be fresher. Windows are anchored on the newest decision in the data, not today, so "last 6 months" is always a fully covered window.

What an LCA is not. An LCA is a filing an employer makes before an H-1B petition. It covers new hires, renewals and transfers, and some never become petitions. Treat LCA counts as an upper bound on sponsorship activity. The USCIS columns are the confirmed side.

Denial-rate flags. Each employer's USCIS denial rate over the last full fiscal year plus the current one is compared with the national rate over the same years (2.5% for FY2025–FY2026 Q3):

FlagRuleWhere that sits
🔴 High3× national or more (7.6%+)5.5% of the 5,159 employers with 20+ decisions
🟡 Elevated2× national or more (5.1%+)a further 3.1%
🟢 Normalbelow 2×the median employer is 1.6%

Both flags need at least 20 decisions and 3 denials, so a single denial at a small firm doesn't produce a scary flag. New-employment petitions (the F-1/OPT → H-1B route) get their own rate in company_sponsorship. USCIS doesn't publish job titles, so denial rates are company-wide even when you filter by role.

"Still sponsoring?" is a rule, not a model. It looks at certified LCAs in the last 6 months against the same months a year earlier:

VerdictRule
🟢 Yes, active5+ filings, and not down by half or more
🟡 Slowing sharply (−x%)at least 10 filings a year earlier, and down 50% or more
🟡 Occasional1–4 filings
🔴 No filings in last 6 monthsnone in 6 months, some in the last 12
🔴 No recent sponsorshipnone in 12 months

How it's built

mermaid
flowchart LR    A[DOL LCA xlsx<br/>5 files, 1 GB] --> C[Standardise<br/>de-duplicate by case number<br/>drop contact details]    B[DOL PERM xlsx] --> C    U[USCIS crosstab<br/>UTF-16 export] --> C    C --> E[Employer resolution<br/>names + federal tax IDs]    E --> D[(sponsors.duckdb<br/>111 MB)]    D -->|gzip, GitHub release| R[Release asset]    R -->|downloaded once,<br/>update check daily| S[MCP server<br/>stdio, read-only]
  • Employer names are messy. Names that file under a shared federal tax ID are merged: "Amazon.com Services LLC", "AMAZON.COM SERVICES LLC", "Amazon Web Services, Inc." and "Amazon Development Center U.S., Inc." count as one employer. Two guards stop bad merges. A tax ID shared by more than 12 names is almost always a payroll provider, so it's ignored. And a name only joins a tax ID's cluster if its first word matches the main filer's. 1.28M filings resolve to 156,896 employers.
  • Search matches whole leading words. "Amazon" finds Amazon.com Services, Amazon Web Services and Amazon Data Services, and lists them, so you can see exactly what was counted. If a name catches something unrelated ("Apple" also matches Apple Tree Dental), find_employer gives you the exact name to pass instead.
  • No personal data ships. The source files include employer contact names, emails and phone numbers, plus attorneys and preparers. The build drops all of it, and a test checks that the published database contains none of those columns.
  • Lean runtime. The server installs only mcp, duckdb and platformdirs. Answers take under a second.

What didn't work (and what replaced it)

  • Tax IDs alone over-merge. The first build merged everything that ever shared a tax ID, and "Amazon" came back with TechCorp Solutions Inc. in it: a small firm whose 21 filings list one of Amazon's tax IDs, almost certainly a keying error. A name now joins a tax ID's cluster only if its first word matches the main filer's. A regression test covers it.
  • "Last 6 months" was off by a day. Subtracting six months from June 30 lands on December 30, so the first window read "Dec 2025–Jun 2026". Windows are now whole calendar months (Jan–Jun 2026 vs Jan–Jun 2025), with tests for month-end and mid-month anchors.
  • Prefix search catches neighbours. "Apple" also matches Apple Tree Dental. Fuzzy brand matching would hide that rather than fix it, so every answer lists the entities it counted, and find_employer gives you the exact name to narrow it.
  • The government sites block scripts. The plan was a scheduled GitHub Action that refreshed the data every quarter. DOL and USCIS both return 403 to automated downloads, and DOL's official data API (45 datasets) doesn't carry LCA or PERM. So the download stays a two-minute browser step, and everything around it is automated: a watcher that says when and what to download, a gated make refresh, and a weekly health check of the published release.

Compared with other H-1B MCP servers

I checked what already existed before building this. As of October 2026:

This serverh1b-job-search-mcph1b-mcpHosted connectors (Pipeworx, MCPBundles)
LCA filings✅ 21 months✅ one quarter at a timevia H1BGrader✅
USCIS approvals and denials✅ by petition type———
Green-card (PERM) filings✅———
Trend vs. a year earlier✅———
Employer names merged via tax ID✅———
Runs locally and offline✅downloads raw files on demandscrapes a third-party sitehosted

Keeping the data fresh

DOL publishes a new quarter about six weeks after it ends. DOL and USCIS refuse scripted downloads, and DOL's data API doesn't include these datasets, so one step stays human: about two minutes in a browser each quarter. Everything else is automated:

mermaid
flowchart LR    W[Watcher<br/>1st and 15th of release months] -->|new data found| N[Notification with<br/>exact files to get]    N --> H[You: download 2-3 files<br/>into ~/Downloads]    H --> R[make refresh]    R --> C[collect, retire old years]    C --> B[rebuild]    B --> G{quality gates}    G -->|fail| X[stop, nothing published]    G -->|pass| D[before/after diff, confirm]    D --> P[GitHub release]    P --> U[users' servers update within a day]    P --> HC[weekly health check in CI]
PieceWhat it does
Watcher (scheduled task)Twice a month in release months, reads the DOL and USCIS pages in a browser and compares them with the published release. Says exactly which files to download, or that nothing's new.
make refreshMoves recognised files from ~/Downloads into data/raw/ (archiving anything replaced, and handling DOL's "Dislclosure" filename typo), keeps the latest three fiscal years, rebuilds, runs the gates, prints a before/after diff, and publishes only after you confirm. make refresh-check stops after the gates.
Quality gatesThe data must move forward. Filings in the period both builds cover must change by less than 2% (a bigger drop means a file went missing). Employer count stays plausible, Microsoft, Amazon and Google are all present, nothing has columns that look like contact details, filings resolve to employers, and all six tools answer.
Weekly health check (data-health.yml)Downloads the published release the way users do and runs the same checks, plus a staleness check (fails if the data is more than 200 days old). A failure emails the maintainer.

Manual steps, if you'd rather not wait for the watcher:

  1. From the DOL performance data page, download the newest LCA_Disclosure_Data_FY… and PERM_Disclosure_Data_FY… files.
  2. From the USCIS H-1B Employer Data Hub, use Crosstab View → the last four fiscal years → Download → CSV.
  3. Run make refresh and enter the USCIS quarter when asked (e.g. FY2026 Q4).

Development

bash
make setup && make test

61 tests run on a synthetic fixture with one planted example of each case: a company that's slowing, one that's steady, one that's stopped, a payroll-provider tax ID, a duplicate case number, a non-H-1B visa class, and a contact-email column that must not survive the build. They also start the real server over stdio and call it as an MCP client would, and they run the refresh pipeline end to end with the gates both passing and failing. CI runs on Python 3.11–3.13, checks that the wheel installs without the build extras, and runs a dependency audit and secret scan.

Code and data are released separately. Data releases are GitHub releases tagged data-YYYY-MM-DD, and the server downloads the newest one. Code releases are version tags: bump the version in pyproject.toml and server.json, then push a tag like v0.1.1, and release.yml publishes to PyPI and the MCP registry through trusted publishing, with no stored tokens.

Not legal advice

This is public filing data. It can't tell you whether a particular team will sponsor a particular role, and policies change. Ask the employer.

Source: README.md at commit b42977b

Tools

6

company_sponsorship

Is a company still sponsoring H-1Bs, and how much? Returns trend tables. Shows certified H-1B LCA filings for the last 6 months, the fiscal year so far and the last full fiscal year (each vs the same period a year earlier), split into new employment and transfers; green-card (PERM) certifications; USCIS petition approvals/denials by fiscal year; and a denial-rate flag (Normal / Elevated / High) relative to the national rate. Point out a High or Elevated flag to the user. Args: company: Employer name as people say it, e.g. "Microsoft", "Amazon", "JPMorgan". role: Optional job filter, e.g. "data scientist", "machine learning", "software engineer". Matches job title and occupation title. state: Optional two-letter worksite state, e.g. "GA". city: Optional worksite city, e.g. "Atlanta".

Parameterscityrolestatecompanyrequired

compare_companies

Compare employers side by side: filings, trend, green cards, denial-rate flag, verdict. Args: companies: Two or more employer names, e.g. ["Stripe", "Databricks", "Snowflake"]. role: Optional job filter, e.g. "data scientist". state: Optional two-letter worksite state. city: Optional worksite city.

Parameterscityrolestatecompaniesrequired

top_sponsors

Rank employers by certified H-1B filings (with denial-rate flags), for a role and place. Args: role: Optional job filter, e.g. "data scientist", "data engineer". state: Optional two-letter worksite state, e.g. "GA". city: Optional worksite city, e.g. "Atlanta". months: Look-back window in whole months ending at the latest data (1-21). limit: Number of employers to return (max 100).

Parameterscityrolelimitstatemonths

salary_range

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.

Parameterscityrolerequiredstatecompany

find_employer

List the legal employer entities a name matches, so you can pick an exact one. Args: query: Start of the employer name, e.g. "Deloitte" or "Amazon Web Services".

Parametersqueryrequired

data_info

Which data the answers come from, the dates covered, and the caveats.

Takes no parameters.

Version history

2
  1. v0.1.1LatestNov 5, 2026
  2. v0.1.0Oct 29, 2026

Score

90% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts
  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts