Taiwan Jobs 台灣求職 MCP server

tw-jobs-mcp

<!-- mcp-name: io.github.HsuanC0921/tw-jobs-mcp -->

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A read-only Model Context Protocol server for Taiwan job boards. It lets Claude, Codex, Gemini, Cursor and any other MCP-capable AI assistant or agent search 104 人力銀行, Yourator and foreign companies' own career sites (Greenhouse, Lever, Workday) for Taiwan job postings and company profiles. More sources may be added later.

Write areas and job categories as plain Chinese names (台北市大安區, 軟體工程師) and the server turns them into 104 codes. No account, no API key.

Unofficial. Not affiliated with or endorsed by 104. It calls 104's public web endpoints. Keep the request rate low and follow 104's terms of use. No job data ships with this project.

Highlights

• Four sources that complement each other: 104 for breadth; Yourator for start-ups and foreign-company offices; career boards (Appier, Ubiquiti, Canonical, Stripe, MongoDB, Pinkoi, Binance and more) for roles that foreign companies post on their own sites and never on 104; and Workday career sites of semiconductor, hardware and pharma employers (NVIDIA, Micron, Applied Materials, KLA, Cisco, Intel, Marvell, NXP, HP, GSK, Novartis and more). Pick one with source, or search them all at once with source="all".
• Six tools, all read-only: searchjobs, getjob, getcompany, salaryoverview, lookup_code, sources. Every tool carries MCP read-only annotations, which clients that gate tool use can read to skip per-call confirmation.
• Filters that really filter: keyword, area, job category, salary floor (monthly, yearly, daily or hourly), job type, remote, posted in the last 7 days, years of experience, education. Each filter was checked against the live site.
• Chinese names in, codes out: ambiguous names return a list of candidates instead of a wrong guess. lookup_code browses the whole area and category tree level by level.
• Built for capable models: a user speaks plainly and the model does the translating. The schema pins every enumerated value, the server instructions state principles (what each source is, how to prepare keywords in both languages, how to convert units) instead of case lists, and the server only handles what the model cannot see: an ambiguous place name is widened to its close matches, a category that cannot be matched becomes a keyword, paging limits are clamped, and an empty search loosens its optional filters one at a time, then tries the other sources. Every assumption is listed in a note under the results.
• Finds the posting you half remember: a ranked 104 search is reordered by how many of your words the title and company cover, which put the intended posting first in 35 of 40 trial searches, up from 18 with 104's own order. Searching several sources ranks the merged rows the same way.
• Chains together: every search result carries id and company_id, so an agent can go posting → details → company without extra lookups.
• Salary market view: salary_overview summarises pay (median, quartiles, min, max) over a sample of postings, grouped by pay unit.
• Frugal with context: replies are compact text, not JSON. A page of 20 postings is a 20-line table of about 2,000 characters (under a fifth of the same data as compact JSON), and the six tool definitions total about 2,000 characters. Long texts are clipped unless you ask for full. Set TWJOBSFORMAT=json for

Shortened. The full README is on GitHub.