ต้นแบบ · รุ่นทดลอง: มีทุก server ในทะเบียนทางการ อัปเดตทุก 30 นาที รีวิวและบัญชีผู้ใช้จะมาในขั้นถัดไป
PopNx MCPที่รวม MCP สำหรับคนใช้ AI · อธิบายเป็นภาษาคน

H1b

โดย pipeworx-ioรุ่น 0.3.1i★ –iบนเครื่องหรือคลาวด์
ชื่อใน registryio.github.pipeworx-io/h1b

ติดตั้ง

ภาพรวม

คำอธิบายจากผู้ทำ (ภาษาอังกฤษตามต้นฉบับ)

H-1B MCP — US H-1B visa sponsorship & wage data from DOL Labor Condition Application (LCA)…

สามเสาของ PopNx MCP

สามเรื่องนี้ไม่รวมเป็นคะแนนเดียว เพราะความเห็น การวัด และความนิยม เป็นคนละเรื่องกัน

ใช้ทำอะไรได้

ตัวนี้ยังไม่ได้เรียบเรียงเป็นภาษาไทย ด้านล่างคือข้อเท็จจริงที่เราอ่านจาก registry

  • การเชื่อมต่อ: บนเครื่องหรือคลาวด์
  • ช่องทาง (transport): stdio, Streamable HTTP
  • หมวด: อื่น ๆ
  • ต้องใส่ค่าเหล่านี้: PIPEWORX_API_KEY

ติดตั้ง

เลือกโปรแกรม AI ที่คุณใช้ แล้วคัดลอกค่าตั้งไปวาง

ก่อนติดตั้ง

ค่าที่ติดป้ายความลับคือกุญแจของบัญชีคุณ อย่าส่งให้ใคร และอย่าวางในแชต

ต้องเตรียมก่อน

  • รันบนคลาวด์ไม่ต้องลงโปรแกรมเพิ่ม ต่อผ่านอินเทอร์เน็ตได้เลย
  • npmติดตั้ง Node.js (มีคำสั่ง npx) ในเครื่อง

คุณใช้แอปไหน

เลือกแอปด้านบน เราจำไว้ให้ ครั้งหน้าปุ่มจะขึ้นว่า “ติดตั้งลง …”

ใช้ได้ทั้ง Claude บนเว็บ แอปคอมพิวเตอร์ และมือถือ:

  1. เปิด Settings → Connectors
  2. กด Add custom connector
  3. วางที่อยู่ด้านล่างแล้วกด Add
  4. ถ้าให้ล็อกอิน ให้ล็อกอินกับผู้ทำ
SettingsConnectors1+ Add2https://…3
https://gateway.pipeworx.io/h1b/mcp

หรือรันในเครื่องคุณ

เปิด Claude Desktop → Settings → Developer → Edit Config แล้ววางค่านี้ในไฟล์ claude_desktop_config.json

{
  "mcpServers": {
    "h1b": {
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-h1b"
      ],
      "env": {
        "PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
      }
    }
  }
}

พิมพ์คำสั่งนี้ใน Terminal

claude mcp add --transport http h1b https://gateway.pipeworx.io/h1b/mcp

หรือรันในเครื่องคุณ

claude mcp add h1b --env PIPEWORX_API_KEY='ใส่ค่าของคุณ' -- npx -y @pipeworx/mcp-h1b

เติมค่าที่เว้นว่างไว้ก่อนรัน

เปิดใน Cursor

หรือคัดลอกค่าตั้งเอง

วางในไฟล์ .cursor/mcp.json ของโปรเจกต์ หรือ ~/.cursor/mcp.json

{
  "mcpServers": {
    "h1b": {
      "url": "https://gateway.pipeworx.io/h1b/mcp"
    }
  }
}

หรือรันในเครื่องคุณ

เปิดใน Cursor

หรือคัดลอกค่าตั้งเอง

วางในไฟล์ .cursor/mcp.json ของโปรเจกต์ หรือ ~/.cursor/mcp.json

{
  "mcpServers": {
    "h1b": {
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-h1b"
      ],
      "env": {
        "PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
      }
    }
  }
}

เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง

เปิดใน VS Code

หรือคัดลอกค่าตั้งเอง

วางในไฟล์ .vscode/mcp.json ของโปรเจกต์

{
  "servers": {
    "h1b": {
      "type": "http",
      "url": "https://gateway.pipeworx.io/h1b/mcp"
    }
  }
}

หรือรันในเครื่องคุณ

เปิดใน VS Code

หรือคัดลอกค่าตั้งเอง

วางในไฟล์ .vscode/mcp.json ของโปรเจกต์

{
  "servers": {
    "h1b": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-h1b"
      ],
      "env": {
        "PIPEWORX_API_KEY": "ใส่ค่าของคุณ"
      }
    }
  }
}

เบราว์เซอร์จะถามก่อนเปิดแอป จากนั้นแอปแสดงค่าตั้งให้ตรวจแล้วกดยืนยัน ช่อง key เว้นว่างไว้ให้คุณใส่เอง

ChatGPT ต่อได้เฉพาะ server ที่อยู่บนอินเทอร์เน็ต:

  1. เปิด ChatGPT บนเว็บ → Settings → Connectors (Apps)
  2. กด Create หรือ Add (บางแพ็กเกจต้องเปิด Developer mode ใน Advanced ก่อน)
  3. วางที่อยู่ด้านล่าง ตั้งชื่อ แล้วบันทึก
  4. ถ้าให้ล็อกอิน ให้ล็อกอินกับผู้ทำ
SettingsConnectors1+ Create2https://…3
https://gateway.pipeworx.io/h1b/mcp

ชื่อเมนูอาจต่างกันเล็กน้อยตามแพ็กเกจและรุ่นของแอป

ตัวเลือกเก็บในคุกกี้บนเครื่องนี้ ไม่ต้องมีบัญชี

ค่าที่ต้องใส่เอง

  • PIPEWORX_API_KEY ความลับ env
    Optional Pipeworx API key. Without it calls are anonymous and capped daily; a free key raises the cap.

ค่าตั้งนี้สร้างจากข้อมูลใน registry ของผู้ทำ ถ้าไม่ตรงกับ README ให้เชื่อ README

ทำอะไรได้บ้าง

ชื่อและคำอธิบายเครื่องมือตามที่ server ตอบมาเมื่อ 6 พ.ย. 2569 ผู้ทำเป็นคนเขียน ไม่ใช่เรา แสดงเป็นข้อความล้วน

  • ask_pipeworx

    PREFER 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_beta

    Beta 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_grounded

    Hallucination-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_within

    Semantic 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_research

    ACCOUNT 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_tools

    Find 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

  • subscribe

    Create 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

  • unsubscribe

    Cancel 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_subscriptions

    List 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_alerts

    Pull 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_facts

    TYPED, 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_search

    Search 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_dependency

    Composite "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_research

    Research 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_arbitrage

    Find 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_edges

    Scan 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_spread

    Cross-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_risk

    Realizable-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_tracker

    Edge 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_edge

    Prices 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_audit

    Extract 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_diff

    Field-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_markets

    JOIN 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_calibration

    REALIZED 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_compare

    The 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_rate

    How 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_evidence

    What 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_compare

    What 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_row

    Claygent-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_row

    Cheap 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_trending

    What 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_questions

    What 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_txt

    Generate 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_check

    Probe 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_presence

    Compare 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_feedback

    Tell 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

  • remember

    Save 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

  • recall

    Retrieve 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

  • forget

    Delete 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_sponsorship

    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-sala

  • h1b_salary

    Look 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_sponsors

    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

ผลตรวจเบื้องต้นi

เราตรวจจากข้อมูลที่ใครก็เปิดดูได้ ยังไม่ได้อ่านโค้ด ข้อที่ไม่เกี่ยวกับตัวนี้ไม่นับในตัวหาร · ตรวจเมื่อ 7 พ.ย. 2569

  • ✓ ผ่าน

    ตัวตนผู้ทำตรงกับ repo

    ชื่อใน registry ยืนยันด้วยบัญชี GitHub pipeworx-io ซึ่งเป็นเจ้าของ repo

  • … ยังไม่ตรวจ

    ยังมีคนดูแล

    ยังไม่ได้อ่านข้อมูล repo จาก GitHub

  • … ยังไม่ตรวจ

    มีสัญญาอนุญาต (license)

    ยังไม่ได้อ่านข้อมูล repo จาก GitHub

  • ✓ ผ่าน

    endpoint ตอบ

    ตอบกลับใน 755 ms (HTTP 200)

  • – ไม่เกี่ยว

    ล็อกอินแบบ OAuth

    ไม่ต้องล็อกอิน

  • ✓ ผ่าน

    คำอธิบายเครื่องมือไม่มีคำสั่งแอบแฝง

    อ่านคำอธิบายเครื่องมือ 47 ตัวแล้ว ไม่พบคำสั่งแอบแฝงถึง AI

เทียบกับตัวอื่นในหมวด อื่น ๆi

  • ดาวมากกว่า 0% ของอีก 18329 ตัวในหมวดนี้
  • ส่งรุ่นใหม่เข้า registry ล่าสุดกว่า 87% ของอีก 18329 ตัวในหมวดนี้
  • ผลตรวจเบื้องต้นดีกว่าหรือเท่ากับ 100% ของอีก 18329 ตัวในหมวดนี้

เทียบกับทุก server ที่ยังใช้งานในหมวดนี้ทั้งทะเบียน ดาวนับเฉพาะเมื่อผู้ลงทะเบียนเป็นเจ้าของ repo

คำถามที่พบบ่อย

H1b ปลอดภัยไหม?

จากการตรวจเบื้องต้นของเรา ผ่าน 3 จาก 3 ข้อที่ตรวจได้ เรายังไม่ได้อ่านโค้ด ก่อนใส่กุญแจหรือ token ให้ดูว่า server ขอสิทธิ์อะไร และเลือกกุญแจที่ทำได้เท่าที่จำเป็น

ต้องใช้กุญแจหรือรหัสอะไรบ้าง?

ต้องใส่ค่าเหล่านี้: PIPEWORX_API_KEY

ตัวนี้รันบนเครื่องหรือบนคลาวด์?

ได้ทั้งสองแบบ จะลงในเครื่องหรือต่อผ่านคลาวด์ก็ได้

ยังมีคนดูแลอยู่ไหม?

repo บน GitHub มีการแก้โค้ดล่าสุด – และส่งรุ่นล่าสุด (0.3.1) เข้า registry 5 วันที่ผ่านมา

ใช้กับโปรแกรมไหนได้บ้าง?

โปรแกรมที่รองรับ MCP เช่น Claude Desktop, Claude Code, Cursor และ VS Code ตัวนี้ต่อผ่าน URL จึงใช้กับโปรแกรมที่รับ remote MCP ได้ เช่น ChatGPT ที่เปิดโหมดนักพัฒนา

มีตัวไหนใช้แทนได้บ้าง?

ในหมวดเดียวกันมี Spec Driven Development, Remote Desktop Commander, Excaliself

ตัวที่ใช้แทนได้

server หมวดเดียวกันจากผู้ทำคนอื่น เรียงตามดาว

อ่านการ์ด:★ ดาว GitHubiสามเสา: รีวิว · ผลตรวจ · การใช้งานiอัปเดต = วันที่ส่งรุ่นล่าสุดเข้า registryi

Spec Driven Development

โดย formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

รันบนเครื่อง★ 440MITอัปเดต 14 เดือนที่ผ่านมา
ติดตั้ง

Remote Desktop Commander

โดย desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

รันบนคลาวด์★ 211อัปเดต เมื่อวานซืน
ติดตั้ง

Excaliself

โดย maciekzygmunt

Find, read, create, edit, and organize Excalidraw drawings in your self-hosted Excaliself instance.

รันบนคลาวด์อัปเดต วันนี้
ติดตั้ง

README จากผู้ทำ

เปิดอ่าน README

ส่วนนี้คัดจาก repo ของผู้ทำตามต้นฉบับ PopNx MCP ไม่ได้ตรวจหรือรับรองเนื้อหา ตัด HTML รูปภาพ และสคริปต์ออกแล้ว ลิงก์จะพาออกนอกเว็บ

@pipeworx/h1b

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.

Tools

  • 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.

Two-tier source (fleet #2514)

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.

PERSONAL-DATA RULE (mandatory, local-copy rule — task #2514)

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.

Data source

  • https://www.dol.gov/agencies/eta/foreign-labor/performance — DOL OFLC performance-data page, links each fiscal year's disclosure file.
  • Verified live 2026-09-29 (not documented by DOL, reverse-engineered from `decision_date` distributions — the record-layout PDF's cover text is WRONG about this for closed years):
  • The current, still-open fiscal year's latest quarterly file is cumulative from Oct 1 of that FY through the end of the named quarter (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.
  • Once a fiscal year closes, DOL's archived quarterly files for that year are each discrete — 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.
  • No auth, no rate limit observed. US federal administrative data — public domain.

Ingest harness

  • 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.
Observed sizes/rows (live, 2026-09-29 — proof-of-transform run, not a full load)
FileBytesReal data rows
FY2026_Q3 (cumulative, Oct'25-Jun'26)251,850,891437,496
FY2025_Q4 (Jul-Sep'25 only)79,134,156118,580
FY2025_Q3 (Apr-Jun'25 only)143,867,250238,425
FY2025_Q2 (Jan-Mar'25 only)106,951,050not downloaded — est. ~175k by size ratio
FY2025_Q1 (Oct-Dec'24 only)87,690,398not downloaded — est. ~145k by size ratio
FY2024_Q4 (Jul-Sep'24 only)83,056,843not 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".

Known source data-quality trap

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.

Gotchas

  • The live DOL .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.
  • Do not load more than one file per still-open fiscal year, and do not load a closed year's file without all four of its quarters — see "Data source" above. Getting this wrong either duplicates rows (double-counting an overlapping cumulative + discrete file) or leaves silent gaps.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{

"mcpServers": { "h1b": { "url": "https://gateway.pipeworx.io/h1b/mcp" } } }

What this endpoint actually serves

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.

No MCP client? Call it over HTTP

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":"..."}.

Run it from npm

@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

ตัดมาแค่ช่วงต้น อ่านต่อใน repo

ถาม PopNx