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XJTLUmedia

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.

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

npm version License: MIT


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

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 "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":[...]}'

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

Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - A tier

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