XJTLUmedia/AI-HR-Management-Toolkit

> 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
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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.
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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:
**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 npx command from a directory that contains a package.json (i.e. any project root). The cwd key in .vscode/mcp.json can override the working directory if needed.
**Windsurf / other MCP clients** — Use the same npx pattern above.
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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.
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All 21 MCP Tools
All tools return structured JSON with next_steps hints so the AI knows what to call next.
Resume Parsing & Ingestion
| Tool | What it does | AI? |
|------|-------------|:---:|
| parse_resume | Parse PDF / DOCX / TXT / MD / URL → raw text + contacts, keywords, section map | No |
| batch_parse_resumes | Parse up to 20 files in one call, full pipeline on each | No |
| inspect_pipeline | Run the 5-stage analysis pipeline → confidence scores, entity counts, data quality report | No |
Unified Analysis
| Tool | What it does | AI? |
|------|-------------|:---:|
| analyze_resume | Master analysis tool with selectable aspects: keywords (TF-IDF + bigrams), patterns (date ranges, metrics, team sizes, career trajectory), entities (NER with 12 types + context disambiguation), skills (13 categories with proficiency estimation), experience (structured timeline), similarity (cosine, Jaccard, TF-IDF overlap vs. job description), or all | No |
> analyze_resume consolidates 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
| Tool | What it does | AI? |
|------|-------------|:---:|
| assess_candidate | Score against up to 8 weighted criteria axes → weighted total + pass / review / reject decision | Optional |
Export & Notifications