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

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):

{
  "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 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

| Tool | What it does | AI? | |------|-------------|:---:| | export_results | Export structured parse results to JSON or CSV | No | | send_email | Send results via SMTP (config passed per call — no server-side secrets stored) | No |

ATS — Jobs

| Tool | What it does | AI? | |------|-------------|:---:| | ats_manage_jobs | Full CRUD for job postings: create, read, update, delete, list, search by title/department/status | No |

ATS — Candidates & Pipeline

| Tool | What it does | AI? | |------|-------------|:---:| | ats_manage_candidates | CRUD + analytics: add, update, move stage, bulk-move, filter, rank, compare, recommend stage changes, summarize | No | | ats_analytics | Unified dashboard + pipeline analytics: stage distribution, conversion rates, avg time-in-stage, bottleneck detection, offer acceptance rate | No | | ats_search | Global full-text search across all ATS entities (candidates, jobs, interviews, offers, notes) | No |

ATS — Interviews

| Tool | What it does | AI? | |------|-------------|:---:| | ats_schedule_interview | Create, update, and delete interviews with conflict detection and interviewer availability check | No | | ats_interview_feedback | Submit structured feedback, compute consensus score, summarize feedback across all interviewers | No |

ATS — Offers & Notes

| Tool | What it does | AI? | |------|-------------|:---:| | ats_manage_offers | Full offer lifecycle: draft → pending → approved → sent → accepted / declined / expired | No | | ats_manage_notes | Add, update, search, and delete timestamped candidate notes | No |

ATS — Enterprise HR

| Tool | What it does | AI? | |------|-------------|:---:| | ats_compliance | EEO/EEOC reporting, GDPR export/erasure, audit trail, data retention policies | No | | ats_talent_pool | Passive candidate talent pools (CRM): create pools, add/remove candidates, search, analytics | No | | ats_scorecard | Structured interview scorecards with weighted criteria, per-evaluator scores, aggregate rankings | No | | ats_onboarding | Post-hire onboarding checklists: tasks by category, assignees, progress tracking, overdue alerts | No | | ats_communication | Email templates with {{variable}} interpolation, send/preview, communication history, stats | No |

Testing & Seeding

| Tool | What it does | AI? | |------|-------------|:---:| | ats_generate_demo_data | Generate a realistic sample ATS dataset (jobs, candidates, interviews, offers) for testing | No |

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 → analyze_resume(text, aspects=["skills", "patterns", "similarity"], jobDescription=...)
     → 13 skill categories with proficiency levels
     → career trajectory, metrics, date ranges
     → 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

| Format | Extensions | Parser | |--------|-----------|--------| | PDF | .pdf | pdf-parse v2 | | DOCX | .docx | mammoth | | Plain text | .txt | direct read | | Markdown | .md, .markdown | regex-based | | URL / HTML | any URL string | cheerio |

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:

| Tab | Description | |-----|-------------| | Single Parse | Upload one file or paste a URL. Returns structured data, pipeline visualization, and AI-enhanced analysis | | Batch Parse | Upload up to 20 files. Export to JSON / CSV / PDF or email results | | Chat | Conversational interface with tool access — ask questions about any parsed resume | | ATS | Full pipeline board: jobs, candidates (Kanban), interviews, offers, and analytics dashboard |

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:

| Header | Description | |--------|-------------| | x-api-key | Your AI provider API key | | x-ai-provider | openai / anthropic / google / deepseek / glm / qwen / openrouter / opencodezen | | x-ai-model | Specific model ID |

# 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

| Layer | Technologies | |-------|-------------| | Framework | Next.js 16 (App Router, Turbopack), React 19, TypeScript | | AI | Vercel AI SDK v6, multi-provider (OpenAI, Anthropic, Google, DeepSeek, GLM, Qwen, OpenRouter) | | MCP | @modelcontextprotocol/sdk v1.29 — Streamable HTTP + stdio transports | | Parsing | pdf-parse v2, mammoth, cheerio | | NLP | TF-IDF, NER, cosine similarity, Jaccard index (all in-process, no external services) | | Schema | Zod v4 | | Export | ExcelJS (CSV/XLSX), jsPDF + jspdf-autotable | | Email | Nodemailer | | Styling | Tailwind CSS v4, Framer Motion |

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 21 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/                  # 17 MCP-specific tools
            ├── analyze-resume.ts     # analyze_resume (unified: keywords, patterns, entities, skills, experience, similarity)
            ├── batch-parse.ts        # batch_parse_resumes
            ├── assess-candidate.ts   # assess_candidate
            ├── ats-manage-candidates.ts  # ats_manage_candidates (includes rank/filter/compare/summarize)
            ├── ats-manage-jobs.ts
            ├── ats-manage-offers.ts
            ├── ats-manage-notes.ts
            ├── ats-analytics.ts      # ats_analytics (unified dashboard + pipeline)
            ├── ats-schedule-interview.ts
            ├── ats-interview-feedback.ts
            ├── ats-search.ts
            ├── ats-generate-demo-data.ts
            ├── ats-compliance.ts     # Enterprise: EEO / GDPR / audit
            ├── ats-talent-pool.ts    # Enterprise: passive candidate CRM
            ├── ats-scorecard.ts      # Enterprise: structured scorecards
            ├── ats-onboarding.ts     # Enterprise: onboarding checklists
            └── ats-communication.ts  # Enterprise: email templates & history

License

MIT