Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
H-1B MCP — US H-1B visa sponsorship & wage data from DOL Labor Condition Application (LCA)…
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
PIPEWORX_API_KEY标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
适用于网页版、桌面版和手机版 Claude:
https://gateway.pipeworx.io/h1b/mcp打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"h1b": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-h1b"
],
"env": {
"PIPEWORX_API_KEY": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add --transport http h1b https://gateway.pipeworx.io/h1b/mcpclaude mcp add h1b --env PIPEWORX_API_KEY=your-value -- npx -y @pipeworx/mcp-h1b运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"h1b": {
"url": "https://gateway.pipeworx.io/h1b/mcp"
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"h1b": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-h1b"
],
"env": {
"PIPEWORX_API_KEY": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"h1b": {
"type": "http",
"url": "https://gateway.pipeworx.io/h1b/mcp"
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"h1b": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-h1b"
],
"env": {
"PIPEWORX_API_KEY": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
ChatGPT 只能连接互联网上的服务器:
https://gateway.pipeworx.io/h1b/mcp菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
工具名称和描述与服务器在 2026年11月6日 报告的完全一致。这些内容由作者撰写,不是我们写的;我们以纯文本形式显示。
ask_pipeworxPREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring au
ask_pipeworx_betaBeta version of ask_pipeworx: identical universal router (same 7,176 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so
ask_pipeworx_groundedHallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 7,176 across 1863 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbati
search_withinSemantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into th
deep_researchACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings,
discover_toolsFind tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real est
resolve_entity"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools re
compare_entities"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing
subscribeCreate a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = o
unsubscribeCancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.
list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
recent_alertsPull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp).
entity_profile"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over cha
recent_changes"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings s
validate_claim"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is f
company_factsTYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the pe
company_register_searchSearch the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want
scan_dependencyComposite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular
bet_researchResearch a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-kristi-noem-win-the-2028-republican-presidential-nomination"), a polymarket.com URL, or a question text. A dated slug ("...-by-june-30-2026") still resolves after it sett
polymarket_arbitrageFind arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `ev
polymarket_edgesScan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segme
polymarket_kalshi_spreadCross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says s
polymarket_fill_riskRealizable-vs-theoretical edge check against live CLOB order-book depth — AND Polymarket's own taker fee, which is never zero and is folded in rather than left for you to add (fleet #3446: r/PredictionsMarkets flagged a "clean" fill that quietly omitted it as optimistic in the di
polymarket_edge_trackerEdge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: d
kalshi_weather_edgePrices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob
resolution_auditExtract the settlement clause of a single Polymarket (.com or .us) or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at (falling back to the market TITLE when the clause itself says "the time range specified in the title" — e.g. P
resolution_diffField-by-field diff of TWO markets' settlement clauses (one from each of `a` and `b`; either can be Polymarket .com, Polymarket US, or Kalshi) — runs resolution_audit on both sides and compares source, settle time, precision, comparison_basis, and evidence standard. Returns `equi
release_calendar_marketsJOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are c
prediction_calibrationREALIZED win rate by shown price and minutes elapsed for short-horizon crypto up/down markets, net of taker fee — read from our own settled-outcomes archive, not from live prices. Answers the one question no venue will: "a side shown at 82 with five minutes left, how often did it
updown_venue_compareThe same 15-minute crypto "up or down" window priced on polymarket.com, Polymarket US and Kalshi, side by side, with whether each pair is actually the same contract. Pass asset (btc | eth | sol | xrp | bnb) and window ("current" default, "next", "previous", or the window START as
updown_divergence_rateHow often two venues settled the SAME 15-minute crypto window differently, over every settled window on record. Compares polymarket.com (Chainlink TWAP) with Kalshi and with Polymarket US (both CF Benchmarks BRTI), and Kalshi with Polymarket US as the same-feed control. Per pair:
archive_evidenceWhat did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy
archive_compareWhat changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and b
research_rowClaygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code in
classify_rowCheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, e
pipeworx_trendingWhat other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice
suggest_questionsWhat can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed e
generate_llms_txtGenerate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at si
ai_visibility_checkProbe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns pe
scan_competitor_ai_presenceCompare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our co
pipeworx_feedbackTell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipewor
rememberSave data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-v
recallRetrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous
forgetDelete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
h1b_employer_sponsorshipCheck 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-sala
h1b_salaryLook 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
h1b_top_sponsorsFind 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
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
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已读取 47 个工具描述,未发现针对 AI 的隐藏指令
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:PIPEWORX_API_KEY
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(0.3.1)于6天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:Spec Driven Development, Remote Desktop Commander, aamio rendezvous
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Meet an agent you have not met, exchange messages that expire, and prove it happened. No account.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
US H-1B visa sponsorship and wage data from DOL Labor Condition Application (LCA) disclosures — "does company X sponsor H-1B", "what does role Y pay at company X", "who sponsors the most data engineers in Austin".
Part of Pipeworx — an MCP gateway connecting AI agents to 1865+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.
h1b_employer_sponsorship(employer, year?) — filing count, salary range, top titles/locations for an employer.h1b_salary(job_title, employer?, city?, year?, limit?) — real disclosed base-salary stats + a sample of records for a role.h1b_top_sponsors(job_title, city?, year?, limit?) — employers ranked by filing count for a role, with median salary.Argument names/shapes are unchanged from before fleet #2514 — only where the answer is served from changed.
Primary: DOL's own LCA disclosure files for recent federal fiscal years. Older years come from h1bdata.info. Every DOL-sourced response carries data_as_of.
Fallback: h1bdata.info, a third-party aggregator of the same DOL data. Used only when the requested year is outside the local mirror's loaded window, or the DB is unreachable — this pack must never regress below its pre-#2514 behavior while the mirror is partially backfilled. Every response carries a source field saying which one actually answered it; the pack never silently swaps sources without saying so.
A fallback zero is never presented as a confident "no" (fleet #3198). If the DB read itself failed this call (no credentials, a timeout, or a transient error — source_status: "primary_unreachable"), a zero-row h1bdata.info answer returns sponsors_h1b: null and a message saying the primary source was never actually consulted, not disproven. Only when the primary source answered fine but the year is genuinely outside its loaded window (source_status: "out_of_window") does a fallback zero stand as a real (if third-party, lag/rename-prone) "no". dbRpc retries once immediately on a transient failure before falling back at all, so a single network blip doesn't by itself demote a mirror-covered query to the weaker source. h1b_employer_sponsorship's matched_employers is also now ranked exact-match first (then name-contains variants), each tagged name_match, since the employer-name match is a plain substring and can legitimately pull back more than one distinct legal name.
Why a mirror at all: the live h1bdata.info scrape caps at 20,000 raw HTML table rows per query (fetchLcaLive's safety cap) — for any employer or job title with more filings than that, salary stats and top_sponsors rankings were being computed from a silently truncated, non-representative slice. For DOL-sourced years, top_sponsors aggregates over every matching row, with no such cap (see total_all_filings in 222_h1b_lca_disclosures.sql's h1b_top_sponsors RPC, a window-function sum computed before the row-limit).
Fiscal year, not calendar year, on the local mirror. DOL's files are organized by federal fiscal year (Oct 1 - Sep 30); year is passed straight through as that fiscal year when served locally. h1bdata.info's own "year" filter is a different, unverified semantic (most likely calendar year) — a query answered by each path for the same year value is not guaranteed to cover the identical 12 months. The response's note/source fields say which happened; this is a real, disclosed difference, not papered over.
DOL's own record layout (LCA_Record_Layout_FY2024_Q4.pdf, read in full 2026-09-29) carries named-individual contact fields that are never read into the mirror — scripts/h1b-lca-transform.py uses an ALLOWLIST of source columns (not a blocklist), so a column DOL adds later is excluded by default:
EMPLOYER_POC_{LAST,FIRST,MIDDLE}_NAME, EMPLOYER_POC_EMAIL, EMPLOYER_POC_PHONE — the employer's named point of contact.AGENT_ATTORNEY_{LAST,FIRST,MIDDLE}_NAME, AGENT_ATTORNEY_EMAIL_ADDRESS, AGENT_ATTORNEY_PHONE — the attorney/agent representing the employer.PREPARER_{LAST,FIRST,MIDDLE}_NAME, PREPARER_EMAIL — whoever filled out the form.DOL's file already excludes the foreign worker's own name/address (DOL's own cover note names only "Attorney's FEIN and Attorney's State Bar Number" as withheld — no worker PII was ever in this dataset). Kept: EMPLOYER_NAME / TRADE_NAME_DBA and employer address down to city/state/country (no street), plus job/wage/worksite fields — this identifies the employer making a mandatory public disclosure, not a private individual.
https://www.dol.gov/agencies/eta/foreign-labor/performance — DOL OFLC performance-data page, links each fiscal year's disclosure file.FY2026_Q3.xlsx spans Oct 2025-Jun 2026). Lives at https://www.dol.gov/media/LCA_Disclosure_Data_ .xlsx. Load only the single latest file for an open year.decision_date falls only within that one quarter (verified: FY2025_Q3.xlsx = Apr-Jun 2025 only, FY2025_Q4.xlsx = Jul-Sep 2025 only — NOT the full year, despite the record-layout PDF literally stating "Reporting Period: October 1 ... through September 30" on its cover page). Lives at https://www.dol.gov/sites/dolgov/files/ETA/oflc/pdfs/LCA_Disclosure_Data_ .xlsx. A closed year needs all four of its quarter files for full coverage; they do not overlap.supabase/migrations/222_h1b_lca_disclosures.sql — h1b_lca_disclosures table + h1b_lca_ingest_runs bookkeeping + three RPCs (h1b_lca_search, h1b_top_sponsors, h1b_lca_coverage). RLS on; anon/authenticated have no grants; service_role only.scripts/h1b-lca-transform.py — streams one .xlsx file row-by-row via openpyxl(read_only=True) (never buffers a whole workbook — a full fiscal year runs 80-250MB uncompressed), allowlist-selects the non-personal columns, and computes base_salary_annual from WAGE_RATE_OF_PAY_FROM/WAGE_UNIT_OF_PAY (Hour×2080, Week×52, Bi-Weekly×26, Month×12, Year×1).scripts/h1b-lca-upsert.sh — per file: download (tries the current-year URL, falls back to the closed-year archive URL), transform, \copy into a staging table, then INSERT ... ON CONFLICT (case_number) DO UPDATE (CASE_NUMBER is DOL's own stable per-application id — no synthetic key needed)..github/workflows/h1b-lca-refresh.yml — workflow_dispatch only (deliberately no schedule: — Bruce's "supa stagger" rule queues big Supabase loads one at a time, overnight, on purpose; GitHub's schedule: trigger has also been unreliable fleet-wide since ~2026-09-09). Default files input is the "latest 8 quarters" the task asked for, with verified zero overlap and zero gap: 2026:FY2026_Q3 (cumulative, = FY2026 Q1+Q2+Q3) + 2025:FY2025_Q1,2025:FY2025_Q2,2025:FY2025_Q3,2025:FY2025_Q4 (four discrete quarters) + 2024:FY2024_Q4 (one discrete quarter) — 6 files, Jul 2024-Jun 2026.| File | Bytes | Real data rows |
|---|---|---|
| FY2026_Q3 (cumulative, Oct'25-Jun'26) | 251,850,891 | 437,496 |
| FY2025_Q4 (Jul-Sep'25 only) | 79,134,156 | 118,580 |
| FY2025_Q3 (Apr-Jun'25 only) | 143,867,250 | 238,425 |
| FY2025_Q2 (Jan-Mar'25 only) | 106,951,050 | not downloaded — est. ~175k by size ratio |
| FY2025_Q1 (Oct-Dec'24 only) | 87,690,398 | not downloaded — est. ~145k by size ratio |
| FY2024_Q4 (Jul-Sep'24 only) | 83,056,843 | not downloaded — est. ~120k by size ratio |
Estimated total for the default 6-file load: ~1.2-1.3M rows, ~650MB of xlsx downloads. Every .xlsx sheet carries a large trailing block of genuinely-empty phantom rows past the real data (e.g. FY2026_Q3's declared sheet dimension is 1,032,736 rows for 437,496 real ones) — this is a DOL export artifact, not a bug in the transform script; those rows are dropped and counted as "without CASE_NUMBER".
DOL's raw WAGE_RATE_OF_PAY_FROM/WAGE_UNIT_OF_PAY occasionally disagree in a way that produces an absurd base_salary_annual (an observed FY2026_Q3 row annualizes to ~$453M — almost certainly a unit-entry error by the filer, kept verbatim rather than "corrected", same discipline as sba_loans keeping literal duplicate/conflicting source rows). median-based stats are resistant to this; a max in a response can occasionally show one of these outliers — that is the source data, not a mirror defect.
.xlsx header spells the H-1B-dependent-employer column H_1B_DEPENDENT (underscore) — the *record-layout PDF* spells it H-1B_DEPENDENT (hyphen). Trust the live file; verified against FY2026_Q3.xlsx 2026-09-29.Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": { "h1b": { "url": "https://gateway.pipeworx.io/h1b/mcp" } } }
tools/list at https://gateway.pipeworx.io/h1b/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": { "pipeworx": { "url": "https://gateway.pipeworx.io/mcp" } } }
Both URLs reach the same gateway and the same 1865+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.
curl -X POST https://gateway.pipeworx.io/v1/tools/h1b_employer_sponsorship \
-H 'Content-Type: application/json' \ -d '{"employer":"Google","year":2024}'
No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/h1b_employer_sponsorship. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.
@pipeworx/mcp-h1b is a local stdio bridge to the Pipeworx gateway: your MCP client starts it with npx, and it forwards every call to the same `ht
只显示了开头部分;其余内容请在仓库中阅读