AI-HR-Management-Toolkit

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.

<img width="1889" height="781" alt="image" src="https://github.com/user-attachments/assets/572b4dd8-8fd4-469c-b71d-a4f513c4b466" /> <img width="1896" height="635" alt="image" src="https://github.com/user-attachments/assets/aa0fc7c1-6373-4a48-9faf-3b15c42871f1" /> <img width="1562" height="572" alt="image" src="https://github.com/user-attachments/assets/4a0ec218-b61f-43c8-b6b8-657219e30dab" />

Live demo: https://ai-hr-management-toolkit.vercel.app

 

<a href="https://glama.ai/mcp/servers/mcp-ai-hr-management-toolkit"> <img width="380" height="200" src="https://glama.ai/mcp/servers/mcp-ai-hr-management-toolkit/badge" alt="mcp-ai-hr-management-toolkit server" /> </a>

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

Example usage:

<img width="1101" height="556" alt="image" src="https://github.com/user-attachments/assets/34a8fd29-5f51-4f8b-9f3c-df0e31f36354" />

<img width="1094" height="314" alt="image" src="https://github.com/user-attachments/assets/fb641f07-a977-413c-903c-b67f806d75b1" />

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

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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):

{
  "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: ``bash 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.

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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

| 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.

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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"

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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

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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

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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

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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.

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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 "file=@resume.pdf"

# Batch parse (up to 20 files)
curl -X POST http://localhost:3000/api/batch-parse \
  -H "x-api-key: sk-..." \
  -F "files=@resume1.pdf" \
  -F "files=@resume2.docx"

# 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":[...]}'

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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 |

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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

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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

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License

MIT