The AI HR Management Toolkit provides 21 MCP tools for AI-powered resume parsing, candidate analysis, and full Applicant Tracking System (ATS) management — primarily algorithmic, with no mandatory AI/API keys required.
Resume Parsing & Analysis
Parse PDF, DOCX, TXT, MD files or URLs into structured JSON (contacts, keywords, section maps)
Batch parse up to 20 resumes in a single call
Run a 5-stage pipeline (Ingestion → Sanitization → Tokenization → Classification → Serialization) with confidence scoring and data quality reports
Extract keywords (TF-IDF), detect patterns (dates, metrics, career trajectory), classify entities via NER (12 types), and extract structured skills (13 categories) with proficiency estimates
Extract structured work experience (titles, organizations, durations, achievements, technologies)
Compute resume-to-job-description similarity using cosine similarity, Jaccard index, TF-IDF overlap, and skill gap analysis
Run all of the above in a single
analyze_resumecall
Candidate Scoring & Assessment
Score candidates against up to 8 weighted criteria axes (Education, Experience, Skills, Certifications, etc.)
Generate pass/review/reject decisions algorithmically or via optional LLM-based assessment
Rank, filter, compare, and recommend stage changes for candidates
Full ATS Management
Jobs: Create, read, update, delete, list, search, close, and reopen job postings
Candidates & Pipeline: Add, update, delete, bulk-move candidates through stages (applied → screening → interview → offer → hired); view pipeline analytics with stage distribution, conversion rates, time-in-stage, and bottleneck detection
Interviews: Schedule with conflict detection; submit, retrieve, and analyze feedback with consensus scores and hiring signal summaries
Offers: Manage full lifecycle (draft → pending → approved → sent → accepted/declined/expired), plus comparison and validation
Notes: Add, update, list, search, and bulk-add timestamped candidate notes
Global Search: Full-text search across all ATS entities
Enterprise HR Features
EEO/EEOC compliance reporting, GDPR data export/erasure, audit trails, and data retention policies
Talent pools for passive candidate CRM
Structured interview scorecards with weighted criteria and aggregate rankings
Post-hire onboarding checklists with task assignment, progress tracking, and overdue alerts
Email templates with variable interpolation, send/preview, and communication history
Export, Communication & Integration
Export results to JSON or CSV
Send results via SMTP email (credentials passed per call, no server-side storage)
Works with MCP clients (Claude Desktop, Cursor, VS Code Copilot, Windsurf) and multiple AI providers (OpenAI, Anthropic, Google, DeepSeek, etc.)
Deployable via NPX, global install, remote HTTP endpoint, or local development
Includes a web UI with chat interface and ATS dashboard, plus REST API endpoints
Generate realistic demo data for testing and demonstrations
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
npxcommand from a directory that contains apackage.json(i.e. any project root). Thecwdkey in.vscode/mcp.jsoncan 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/mcpTest locally:
npx @modelcontextprotocol/inspector http://localhost:3000/api/mcpOption 4: Local development (Web UI + MCP)
git clone <repo-url>
cd Resume-parser
npm install
npm run devWeb 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 PDF / DOCX / TXT / MD / URL → raw text + contacts, keywords, section map | No |
| Parse up to 20 files in one call, full pipeline on each | No |
| Run the 5-stage analysis pipeline → confidence scores, entity counts, data quality report | No |
Unified Analysis
Tool | What it does | AI? |
| Master analysis tool with selectable aspects: | No |
analyze_resumeconsolidates 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? |
| Score against up to 8 weighted criteria axes → weighted total + pass / review / reject decision | Optional |
Export & Notifications
Tool | What it does | AI? |
| Export structured parse results to JSON or CSV | No |
| Send results via SMTP (config passed per call — no server-side secrets stored) | No |
ATS — Jobs
Tool | What it does | AI? |
| Full CRUD for job postings: create, read, update, delete, list, search by title/department/status | No |
ATS — Candidates & Pipeline
Tool | What it does | AI? |
| CRUD + analytics: add, update, move stage, bulk-move, filter, rank, compare, recommend stage changes, summarize | No |
| Unified dashboard + pipeline analytics: stage distribution, conversion rates, avg time-in-stage, bottleneck detection, offer acceptance rate | No |
| Global full-text search across all ATS entities (candidates, jobs, interviews, offers, notes) | No |
ATS — Interviews
Tool | What it does | AI? |
| Create, update, and delete interviews with conflict detection and interviewer availability check | No |
| Submit structured feedback, compute consensus score, summarize feedback across all interviewers | No |
ATS — Offers & Notes
Tool | What it does | AI? |
| Full offer lifecycle: draft → pending → approved → sent → accepted / declined / expired | No |
| Add, update, search, and delete timestamped candidate notes | No |
ATS — Enterprise HR
Tool | What it does | AI? |
| EEO/EEOC reporting, GDPR export/erasure, audit trail, data retention policies | No |
| Passive candidate talent pools (CRM): create pools, add/remove candidates, search, analytics | No |
| Structured interview scorecards with weighted criteria, per-evaluator scores, aggregate rankings | No |
| Post-hire onboarding checklists: tasks by category, assignees, progress tracking, overdue alerts | No |
| Email templates with | No |
Testing & Seeding
Tool | What it does | AI? |
| Generate a realistic sample ATS dataset (jobs, candidates, interviews, offers) for testing | No |
assess_candidateoptionally calls an LLM when you supplyprovider+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) │
└─────────────┘ └──────────────┘ └──────────────┘ └────────────────┘ └───────────────┘Ingestion — PDF via pdf-parse v2, DOCX via mammoth, HTML/URL via cheerio, plain text/markdown natively
Sanitization — Removes non-ASCII artifacts, normalizes whitespace, strips formatting noise
Tokenization — TF-IDF with unigrams, bigrams, and trigrams; scored by document frequency
Classification — NER with domain-aware disambiguation (e.g. "Java" as language vs. Indonesian city; "Go" as language vs. verb)
Serialization — Maps entities to typed
ResumeSchemawith confidence scores and data quality metrics
Supported File Formats
Format | Extensions | Parser |
| pdf-parse v2 | |
DOCX |
| mammoth |
Plain text |
| direct read |
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 proficiencyWeb 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 |
| Your AI provider API key |
|
|
| 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 |
|
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 |
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 lintProject 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