H-1B Sponsor Search — MCP Server

An MCP server that lets an AI assistant query USCIS H-1B Employer Data Hub records: search a company's sponsorship history, rank the largest sponsors in a given US state, and read a summary of the dataset.

Built with the MCP Python SDK (mcp >= 2.0) and the Python standard library — no pandas, no fuzzy-matching dependency.

Capabilities

Kind	Name	Purpose
Tool	search_sponsors(company_name, limit=10)	Find employers by full or partial name. Case- and punctuation-insensitive, so amazon.com matches AMAZON COM SERVICES LLC. Totals are aggregated across all of an employer's offices.
Tool	get_top_sponsors_by_state(state, limit=10)	Rank the largest sponsors in one state by approved petitions, counting only petitions filed from offices in that state.
Resource	h1b://summary	Dataset coverage, unique employer count, national approval/denial totals and approval rate.

Requirements

• Python 3.13+
• uv
• Node.js 18+ — only if you want to run the MCP Inspector

Setup

git clone https://github.com/calvinlee326/h1b-sponsor-mcp.git
cd h1b-sponsor-mcp
uv sync

uv sync creates .venv and installs the exact versions pinned in uv.lock.

Getting the data

The USCIS export is not redistributed in this repository. Download it yourself:

1. Open the H-1B Employer Data Hub.

2. Run a query (leave the filters empty for the full dataset) and export the results. You will get a file named Employer Information.csv.

3. Move it into place:

   mv ~/Downloads/"Employer Information.csv" data/h1b_employers.csv
   

uscis.gov blocks non-browser clients. curl, wget and scripted fetches return HTTP 403 regardless of User-Agent, so this step has to be done in a real browser.

About the file format

Despite the .csv extension, the export is UTF-16 encoded and tab-delimited. Opening it with open(path) or parsing it with a comma delimiter will not work. The loader in server.py handles both.

Expected columns:

Line by line, Fiscal Year, Employer (Petitioner) Name, Tax ID,
Industry (NAICS) Code, Petitioner City, Petitioner State, Petitioner Zip Code,
New Employment Approval,            New Employment Denial,
Continuation Approval,              Continuation Denial,
Change with Same Employer Approval, Change with Same Employer Denial,
New Concurrent Approval,            New Concurrent Denial,
Change of Employer Approval,        Change of Employer Denial,
Amended Approval,                   Amended Denial

USCIS splits petitions into six approval/denial categories. This differs from the annual bulk export also published by USCIS, which uses Employer, State, Initial Approval and Continuing Approval instead — that file is not compatible with this server without editing the column constants at the top of server.py.

A 40-row sample in the identical format is committed at data/h1b_employers.sample.csv, so the repository is inspectable without the 41 MB download.

Use with Claude Desktop

Add the server to claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "h1b-sponsor": {
      "command": "/absolute/path/to/uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/h1b-sponsor-mcp",
        "h1b-sponsor-mcp"
      ]
    }
  }
}

Then fully quit Claude Desktop and reopen it (Cmd-Q on macOS — closing the window is not enough).

Both paths must be absolute; find your uv with which uv. --directory is required because Claude Desktop launches servers with the working directory set to /, so uv would otherwise never find this project. h1b-sponsor-mcp is the console script declared in pyproject.toml, which survives moves better than a hardcoded file path.

Once connected, ask things like:

• How many H-1B petitions has Amazon sponsored?
• Who are the top 5 H-1B sponsors in Washington state?
• What's the overall H-1B approval rate in this dataset?

Troubleshooting

Test the server directly, bypassing Claude Desktop:

cd / && echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"t","version":"1"}}}' \
  | /absolute/path/to/uv run --directory /absolute/path/to/h1b-sponsor-mcp h1b-sponsor-mcp

A JSON-RPC response means the server is fine and the problem is in the client config. A traceback means the problem is in the server. Run it from / — testing from inside the project directory can succeed even when the config is wrong, because uv finds pyproject.toml by walking up from the working directory.

Client logs are at ~/Library/Logs/Claude/mcp-server-h1b-sponsor.log.

Because the transport is stdio, anything written to stdout corrupts the protocol stream. Use logging (stderr) when debugging, never print().

Use with MCP Inspector

uv run mcp dev src/h1b_sponsor_mcp/server.py --with-editable .

This opens a browser UI where you can list tools, inspect the JSON schemas generated from the Python type hints, and call tools interactively. The URL printed in the terminal includes an auth token and will not connect without it. --with-editable . installs this project into the Inspector's environment.

mcp dev currently pulls Inspector v1, which is deprecated. For the current version, skip mcp dev and point Inspector at the same command Claude Desktop uses:

npx @modelcontextprotocol/inspector@latest \
  uv run --directory "$PWD" h1b-sponsor-mcp

Testing

uv run pytest

Tests run against the real USCIS export and skip automatically if data/h1b_employers.csv is absent.

How it works

server.py builds two cached indexes on first use, both derived from a single parse of the export:

• employer_index() — one entry per employer, summed across every office and fiscal year. Answers "how many people does Google sponsor" (3,629).
• state_index() — one entry per (employer, state), grouped by state and pre-sorted by approvals. Answers "who sponsors most in Texas" without crediting a national employer's company-wide total to every state it appears in (Google is 3,613 in CA and 16 in TX).

Both use functools.lru_cache, so the parse and aggregation happen on the first tool call rather than at import. This keeps the MCP initialize handshake instant, which matters because clients can time out during startup. A test asserts the two indexes agree: per-state figures must sum to the national total.

Employer names are matched on a normalized key (lowercased, non-alphanumeric characters removed). This also merges duplicate spellings in the source data — AMAZON COM SERVICES LLC and AMAZON.COM SERVICES LLC are separate USCIS rows for one company. Roughly 6,000 such duplicates collapse this way, and any merged spellings are reported in an also_filed_as field.

Known limitations

• Legal suffixes are not stripped. COGNIZANT ... US CORP and COGNIZANT ... US remain separate entries, so some rankings understate a company. Stripping CORP/INC/LLC would fix it but would also merge genuinely distinct subsidiaries. Tax ID cannot disambiguate them because the export masks it to four digits.
• Data is loaded once per process. After replacing the CSV, restart the client to pick up the new file.
• The config uses absolute paths. Moving or renaming the project directory breaks the Claude Desktop entry until the config is updated.

Data source

USCIS H-1B Employer Data Hub — public US government data. Approval and denial counts reflect petitions processed by USCIS, not individual workers; one employer may file several petitions for the same person across categories.