AI HR Management Toolkit

AI HR Management Toolkit

AI-powered resume parser & full Applicant Tracking System with 24 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

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 24 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

23 of 24 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):

{
  "mcpServers": {
    "ai-hr-management-toolkit": {
      "command": "npx",
      "args": ["-y", "mcp-ai-hr-management-toolkit"]
    }
  }
}

Example usage:

Cursor — Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "ai-hr-management-toolkit": {
      "command": "npx",
      "args": ["-y", "mcp-ai-hr-management-toolkit"]
    }
  }
}

VS Code Copilot — Create .vscode/mcp.json in your project root:

{
  "servers": {
    "ai-hr-management-toolkit": {
      "command": "npx",
      "args": ["-y", "mcp-ai-hr-management-toolkit"]
    }
  }
}

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.

Installation Options

Option 1: NPX (Zero-install, recommended)

Works from any project directory (requires a package.json in the working directory):

{
  "mcpServers": {
    "ai-hr-management-toolkit": {
      "command": "npx",
      "args": ["-y", "mcp-ai-hr-management-toolkit"]
    }
  }
}

Option 2: Global install

Install once, use from any directory:

npm install -g mcp-ai-hr-management-toolkit

{
  "mcpServers": {
    "ai-hr-management-toolkit": {
      "command": "mcp-ai-hr-management-toolkit",
      "args": []
    }
  }
}

Option 3: Remote HTTP endpoint

Deploy the Next.js app and use the Streamable HTTP transport:

https://your-domain.com/api/mcp

Test locally:

npx @modelcontextprotocol/inspector http://localhost:3000/api/mcp

Option 4: Local development (Web UI + MCP)

git clone <repo-url>
cd Resume-parser
npm install
npm run dev

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 24 MCP Tools

All tools return structured JSON with next_steps hints so the AI knows what to call next.

Resume Parsing & Ingestion

Text Analysis & NLP

Candidate Matching & Scoring

Export & Notifications

ATS — Jobs

ATS — Candidates & Pipeline

ATS — Interviews

ATS — Offers & Notes

Testing & Seeding

assess_candidate optionally calls an LLM when you supply provider + apiKey; it falls back to fully algorithmic scoring otherwise.

Example Multi-Turn Flow

You: "Parse this resume and tell me if they're a good fit for our Senior Engineer role"

AI → parse_resume(file)
     → raw text, contact info, section map

AI → inspect_pipeline(rawText)
     → 5-stage confidence scores, entity classification

AI → extract_skills_structured(text)
     → 13 skill categories with proficiency levels

AI → detect_patterns(text)
     → career trajectory, metrics, date ranges

AI → compute_similarity(text, jobDescription)
     → cosine 0.74, skill match 82%, gap analysis

AI synthesizes → "Strong match. 6 of 8 required skills present.
                  Two gaps: Kubernetes and system design at scale.
                  Recommend: Technical Screen"

Analysis Pipeline

Every resume runs through a 5-stage algorithmic pipeline:

┌─────────────┐    ┌──────────────┐    ┌──────────────┐    ┌────────────────┐    ┌───────────────┐
│  Ingestion  │───▶│ Sanitization │───▶│ Tokenization │───▶│ Classification │───▶│ Serialization │
│ (file/URL)  │    │ (noise trim) │    │  (TF-IDF)    │    │ (NER + disamb) │    │ (structured)  │
└─────────────┘    └──────────────┘    └──────────────┘    └────────────────┘    └───────────────┘

1. Ingestion — PDF via pdf-parse v2, DOCX via mammoth, HTML/URL via cheerio, plain text/markdown natively
2. Sanitization — Removes non-ASCII artifacts, normalizes whitespace, strips formatting noise
3. Tokenization — TF-IDF with unigrams, bigrams, and trigrams; scored by document frequency
4. Classification — NER with domain-aware disambiguation (e.g. "Java" as language vs. Indonesian city; "Go" as language vs. verb)
5. Serialization — Maps entities to typed ResumeSchema with confidence scores and data quality metrics

Supported File Formats

Max file size: 10 MB

Structured Output Schema

contact        — name, email, phone, location, LinkedIn, GitHub, website, portfolio
summary        — professional summary text
skills[]       — name, category (13 types), proficiency, usage context
experience[]   — company, title, start/end dates, highlights, achievements (with metrics), technologies
education[]    — institution, degree, field, dates, GPA
certifications[] — name, issuer, date, credential URL
projects[]     — name, description, URL, technologies, highlights
languages[]    — spoken language and proficiency

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:

# Parse a single resume
curl -X POST http://localhost:3000/api/parse \
  -H "x-api-key: sk-..." \
  -F "[email protected]"

# Batch parse (up to 20 files)
curl -X POST http://localhost:3000/api/batch-parse \
  -H "x-api-key: sk-..." \
  -F "[email protected]" \
  -F "[email protected]"

# MCP endpoint (Streamable HTTP)
curl -X POST http://localhost:3000/api/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","method":"tools/list","id":1}'

# Export parsed data
curl -X POST http://localhost:3000/api/export \
  -H "Content-Type: application/json" \
  -d '{"format":"csv","results":[...]}'

Tech Stack

Development

npm install

# Start dev server (Web UI at :3000 + MCP at /api/mcp)
npm run dev

# Build the standalone MCP CLI (stdio transport)
npm run build:mcp

# Build the Next.js app for production
npm run build

# Test MCP with the official inspector
npx @modelcontextprotocol/inspector http://localhost:3000/api/mcp
npx @modelcontextprotocol/inspector node dist/mcp-stdio.js

# Lint
npm run lint

Project Structure

src/
├── app/
│   ├── page.tsx              # Main UI (tabs, provider selector, chat, ATS)
│   ├── layout.tsx            # Root layout + global styles
│   └── api/
│       ├── parse/route.ts    # Single resume parse
│       ├── batch-parse/route.ts
│       ├── chat/route.ts     # Conversational AI with tool access
│       ├── mcp/route.ts      # MCP server (Streamable HTTP)
│       ├── models/route.ts   # Provider model listing
│       ├── export/route.ts   # JSON / CSV / PDF export
│       └── email/route.ts    # SMTP email
├── components/               # React UI components (parse, batch, chat, ATS)
│   └── ats/                  # ATS-specific views (Kanban, Dashboard, Scheduler…)
└── lib/
    ├── ai-model.ts           # Multi-provider model config (no env fallback)
    ├── mcp-server.ts         # MCP server — registers all 24 tools
    ├── schemas/
    │   ├── resume.ts         # Zod v4 ResumeSchema
    │   └── criteria.ts       # Assessment criteria schema
    ├── analysis/
    │   ├── pipeline.ts       # 5-stage pipeline orchestrator
    │   ├── sanitizer.ts      # Text cleaning
    │   ├── keyword-extractor.ts  # TF-IDF
    │   ├── classifier.ts     # NER with context disambiguation
    │   ├── pattern-matcher.ts    # Regex extraction (metrics, dates, contacts)
    │   └── scoring.ts        # Cosine similarity, Jaccard, skill matching
    ├── parser/
    │   ├── pdf.ts, docx.ts, text.ts, markdown.ts, url.ts
    │   └── index.ts
    ├── ats/
    │   ├── types.ts          # ATS entity types
    │   ├── store.ts          # In-memory ATS state
    │   ├── demo-data.ts      # Realistic seed data generator
    │   └── context.tsx       # React context for ATS state
    └── tools/
        ├── parse-resume.ts       # parse_resume
        ├── inspect-pipeline.ts   # inspect_pipeline
        ├── export-results.ts     # export_results
        ├── send-email.ts         # send_email
        └── mcp/                  # 20 MCP-specific tools
            ├── extract-keywords.ts
            ├── detect-patterns.ts
            ├── classify-entities.ts
            ├── compute-similarity.ts
            ├── extract-skills-structured.ts
            ├── extract-experience-structured.ts
            ├── analyze-resume-comprehensive.ts
            ├── batch-parse.ts
            ├── assess-candidate.ts
            ├── manage-candidates.ts
            ├── ats-manage-jobs.ts
            ├── ats-manage-candidates.ts
            ├── ats-schedule-interview.ts
            ├── ats-manage-offers.ts
            ├── ats-manage-notes.ts
            ├── ats-interview-feedback.ts
            ├── ats-pipeline-analytics.ts
            ├── ats-dashboard-stats.ts
            ├── ats-search.ts
            └── ats-generate-demo-data.ts

License

MIT