104 Job Hunter MCP
This MCP server enables AI-assisted job hunting on 104 Job Bank, prioritizing local privacy and human review. You can:
Search for jobs by keyword, location, and page, using the backend API directly.
Retrieve job details (description, requirements, benefits) from a posting's URL via a real browser.
Prepare applications with a pre-filled cover letter, open a browser for review, and require human confirmation for final submission; includes a dry-run mode for safe testing. All operations run locally, with persistent login sessions stored in a dedicated Chrome profile.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@104 Job Hunter MCP搜尋台北市的 TypeScript 前端工程師職缺"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
104 Job Hunter MCP
透過 MCP (Model Context Protocol) 讓 AI 直接操作 104 人力銀行。實現搜尋、比對、投遞自動化。 🤖 進階應用:利用
AGENT.md啟動詢答,從現有履歷直接建立「求職者虛擬分身」(MY_PROFILE.md),精準鎖定職缺。
🤖 給 AI Agent 的最高指導原則 (System Prompt for AI)
如果你是接手這個專案的 AI,你的下一步是立刻讀取專案根目錄的 AGENT.md。
裡面有建立「虛擬分身」、過濾職缺與投遞的標準劇本。讀完前,禁止執行任何搜尋工具。
✨ 核心功能 (Features)
🔍 API 攔截式搜尋:直接攔截 104 後端 JSON API,繞過前端防爬蟲機制。
📄 職缺詳情讀取:用真實 Chrome 提取職位描述、條件要求與福利。
🛡️ Hit-in-the-loop 安全機制:投遞工具只會填好求職信,絕對不會幫你按送出。
🍪 持久化登入:Cookie 存入專屬 Chrome Profile,免重複登入。
🤖 Agent 行為規範:內建
AGENT.md提供自動化求職劇本。
Related MCP server: trackly-cli
🔒 隱私與安全 (Privacy & Security)
這個工具採用本地優先 (Local-First) 架構,確保帳號安全:
100% 本地執行:MCP 伺服器只在你的電腦上跑,不回傳帳號密碼。
不經手密碼:登入時會跳出本地 Chrome,你自己在 104 官方頁面登入。
Cookie 安全隔離:加密存在本機的
.chrome-profile/。該資料夾已列入.gitignore,不會上傳 GitHub。
🚀 安裝與執行 (Setup)
1. Clone 專案與建置
git clone https://github.com/YuJunWang/104-job-hunter-mcp.git
cd 104-job-hunter-mcp
npm install
npm run build(系統需求:Node.js 18+、Google Chrome)
2. 登入 104 帳號(只需一次)
npx tsx src/login.ts執行後會跳出 Chrome 視窗,請手動登入 104,完成後關閉視窗。
3. 掛載 MCP 伺服器
把以下設定貼進 MCP 客戶端設定檔(如 Antigravity 的 mcp_config.json 或 Claude Desktop config.json):
{
"mcpServers": {
"104-job-hunter": {
"command": "node",
"args": ["/絕對路徑/104-job-hunter-mcp/build/index.js"]
}
}
}(Windows 使用者請注意路徑反斜線跳脫:C:\\Users\\你的帳號\\...\\build\\index.js)
重啟 AI 客戶端,工具就準備好了。
4. 設定 AI 腦袋 (Agent 規範)
複製根目錄的
MY_PROFILE.example.md,重新命名為MY_PROFILE.md。填入你的求職偏好與地雷。
讓 AI 讀取
AGENT.md:Antigravity:將
AGENT.md放進.agents/rules/,或存成全域 Skill (~/.gemini/config/skills/104-hunter/SKILL.md)。Cursor:把
AGENT.md內容貼進.cursorrules。Claude Desktop:貼進 System Prompt。
直接講:「請閱讀專案內的 MY_PROFILE.md 與 AGENT.md 並按照流程幫我找工作」。
🤖 AI Agent 開發與操作手冊
給 AI 的環境建置交接指令
如果你是負責幫人類安裝 MCP 的 AI,請執行以下步驟:
跑
git clone、npm install跟npm run build。請人類跑
npx tsx src/login.ts並手動登入。幫人類把 MCP 絕對路徑寫進設定檔。
讀取
AGENT.md了解操作劇本。依據
AGENT.md引導人類建立MY_PROFILE.md。
工具列表 (MCP Tools)
job104_check_session
功能:檢查登入狀態。
時機:求職流程的最開頭,確保 Cookie 未過期。
job104_search
必填參數:
keyword(如:AI 全端工程師 Python)選填參數:
location(如:台北市)、page(預設 1)注意:AI 應在背景比對結果,只向人類展示 Top 3~5 筆。
job104_get_details
必填參數:
job_url功能:獲取職位描述、條件要求與福利。草擬推薦信前必備。
job104_prepare_application (Hit-in-the-loop)
必填參數:
job_url選填參數:
cover_letter_text、dry_run(預設 false)注意:只會開啟投遞確認頁。AI 執行後必須提醒人類:「請手動點擊確認送出」。
job104_search_companies
必填參數:
keyword(公司名稱)選填參數:
page、pageSize
job104_get_company_detail
必填參數:
companyInput(代碼或網址)功能:獲取公司福利、產品與開放職缺列表。
job104_save_job
必填參數:
jobInput(代碼或網址)功能:將職缺加入 104 收藏。
job104_save_company
必填參數:
companyInput(代碼或網址)功能:追蹤公司。
📁 專案結構
104-job-hunter-mcp/
├── src/
│ ├── index.ts # MCP 主程式,工具註冊入口
│ ├── browser.ts # Playwright 瀏覽器單例管理 (含 Stealth Plugin)
│ ├── login.ts # 登入輔助腳本
│ ├── test-client.ts # 本地測試客戶端
│ └── tools/
│ ├── search.ts # job104_search
│ ├── details.ts # job104_get_details
│ ├── apply.ts # job104_prepare_application
│ ├── company.ts # job104_search_companies, job104_get_company_detail
│ ├── save.ts # job104_save_job, job104_save_company
│ └── session.ts # job104_check_session
├── build/ # npm run build 輸出
├── .chrome-profile/ # Cookie 儲存目錄 (不進 git)
├── AGENT.md # AI 操作劇本
├── MY_PROFILE.example.md # 虛擬分身範本
├── package.json
└── tsconfig.jsonMaintenance
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