
Ai Hr Management Toolkit
io.github.XJTLUmedia由 GitHub 帳號 XJTLUmedia 發布v3.0.5更新於 Oct 24, 2026
AI HR toolkit: 24 MCP tools for resume parsing, skill extraction & ATS management.
概覽
一個本機 MCP 伺服器,讓 AI 助理解析履歷、擷取技能並為候選人評分,還提供涵蓋職缺、候選人、面試與錄取通知的完整招募管理系統。
- 可以做什麼
- 解析履歷與文件:讀取 PDF、DOCX、TXT、Markdown 和網址內容,輸出結構化 JSON,支援單筆與批次處理,並可檢視分析流程各階段與信心分數。分析候選人:擷取關鍵詞、模式、實體、技能、經歷,以及與職缺描述的相似度。評分與排序:依加權條件評估候選人,給出通過、複核或拒絕的結論。管理招募流程:建立和編輯職缺、候選人、面試、錄取通知與備註,並提供搜尋、分析、評分卡、人才庫、到職清單與法遵報告。匯出與寄送:將結果匯出為 JSON 或 CSV 並以電子郵件寄送。產生測試用範例資料。
- 適用情境
- 適合需要讓助理批次篩選履歷、依職缺描述比較候選人,或在不另外安裝招募系統的情況下維護招募流程的情境。適合在桌面 MCP 用戶端中使用的招募人員與中小型 HR 團隊。
- 執行需求
- 以 npm 套件形式透過 stdio 在本機執行,通常以 npx 啟動,因此需要 Node.js,且工作目錄中要有 package.json。演算法類工具不需要 API 金鑰或環境變數;如需 LLM 輔助評分,可在單次呼叫中提供 AI 服務商金鑰。README 也說明了自行部署的 HTTP 端點方式。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Ai Hr Management Toolkit,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
AI HR Management Toolkit
[image] [image] [image]AI-powered resume parser & full Applicant Tracking System with 21 MCP tools. Parse PDFs, extract skills, detect patterns, score candidates, and manage a complete hiring pipeline — all from your AI assistant, no manual work required.
Live demo: https://ai-hr-management-toolkit.vercel.app
[mcp-ai-hr-management-toolkit server]What Is This?
You have 50 resumes to screen. Your AI assistant can reason about candidates — but it cannot open PDFs, extract structured data, or track pipeline stages. This toolkit bridges that gap.
Give your AI assistant 21 tools covering the entire hiring workflow:
- Parse PDFs, DOCX, TXT, Markdown, and URLs into structured JSON
- Extract skills, experience, keywords, and entities algorithmically
- Score and rank candidates against job descriptions
- Run a full ATS: jobs, candidates, interviews, offers, notes, and analytics
20 of 21 tools are 100% algorithmic — no LLM calls, no API keys required. The AI calls tools, interprets the results, and delivers analysis. You just ask questions.
Quick Start (MCP Clients)
No installation needed. Point your MCP client at the package:
Claude Desktop — Edit %APPDATA%\Claude\claude_desktop_config.json (Windows) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):
Example usage:
[image] [image]Cursor — Add to .cursor/mcp.json in your project root:
VS Code Copilot — Create .vscode/mcp.json in your project root:
VS Code users: Run the
npxcommand from a directory that contains apackage.json(i.e. any project root). Thecwdkey in.vscode/mcp.jsoncan override the working directory if needed.
Windsurf / other MCP clients — Use the same npx pattern above.
Installation Options
Option 1: NPX (Zero-install, recommended)
Works from any project directory (requires a package.json in the working directory):
Option 2: Global install
Install once, use from any directory:
Option 3: Remote HTTP endpoint
Deploy the Next.js app and use the Streamable HTTP transport:
Test locally:
Option 4: Local development (Web UI + MCP)
Web UI at http://localhost:3000. MCP endpoint at http://localhost:3000/api/mcp. No .env needed — configure API keys in the UI or pass them per tool call.
All 21 MCP Tools
All tools return structured JSON with next_steps hints so the AI knows what to call next.
Resume Parsing & Ingestion
Unified Analysis
analyze_resumeconsolidates what were previously 7 separate tools (extract_keywords,detect_patterns,classify_entities,extract_skills_structured,extract_experience_structured,compute_similarity,analyze_resume_comprehensive) into a single entry point with aspect selection.
Candidate Matching & Scoring
Export & Notifications
ATS — Jobs
ATS — Candidates & Pipeline
ATS — Interviews
ATS — Offers & Notes
ATS — Enterprise HR
Testing & Seeding
assess_candidateoptionally calls an LLM when you supplyprovider+apiKey; it falls back to fully algorithmic scoring otherwise.
Example Multi-Turn Flow
Analysis Pipeline
Every resume runs through a 5-stage algorithmic pipeline:
- Ingestion — PDF via pdf-parse v2, DOCX via mammoth, HTML/URL via cheerio, plain text/markdown natively
- Sanitization — Removes non-ASCII artifacts, normalizes whitespace, strips formatting noise
- Tokenization — TF-IDF with unigrams, bigrams, and trigrams; scored by document frequency
- Classification — NER with domain-aware disambiguation (e.g. "Java" as language vs. Indonesian city; "Go" as language vs. verb)
- Serialization — Maps entities to typed
ResumeSchemawith confidence scores and data quality metrics
Supported File Formats
Max file size: 10 MB
Structured Output Schema
Web UI
The app ships with a full web interface:
Switch AI providers from the selector at the top. Supports OpenAI, Anthropic, Google, DeepSeek, GLM, Qwen, OpenRouter, and OpenCode Zen.
REST API Endpoints
All endpoints accept multipart/form-data with optional headers:
Tech Stack
Development
Project Structure
License
來源:README.md,提交 cbeba6c
工具
0版本歷史
1- v3.0.5最新Sep 16, 2026
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