Ai Hr Management Toolkit

io.github.XJTLUmediaPublished by GitHub account XJTLUmediav3.0.5Updated Oct 24, 2026

AI HR toolkit: 24 MCP tools for resume parsing, skill extraction & ATS management.

Overview

AI-generated overview

A local MCP server that gives an AI assistant tools to parse resumes, extract skills and score candidates, plus a full applicant tracking system for jobs, hires

What you can do
Parse resumes and documents: read PDF, DOCX, TXT, Markdown and URL sources into structured JSON, in single or batch mode, and inspect the analysis pipeline stages and confidence scores. Analyze candidates: extract keywords, patterns, entities, skills, experience and similarity to a job description. Score and rank: assess candidates against weighted criteria and get a pass, review or reject decision. Manage the hiring pipeline: create and edit jobs, candidates, interviews, offers and notes, with search, analytics, scorecards, talent pools, onboarding checklists and compliance reporting. Export and send: write results to JSON or CSV and send them by email. Generate demo data for testing.
When to use it
Useful when you want an assistant to screen many resumes, compare candidates against a job description, or keep a hiring pipeline organized without a separate ATS. It suits recruiters and small HR teams working from a desktop MCP client.
Requirements
Runs locally as an npm package over stdio, typically launched with npx, so Node.js and a working directory containing a package.json are needed. No API keys or environment variables are required for the algorithmic tools; an AI provider key can optionally be supplied per call for LLM-assisted scoring. The README also describes a self-hosted HTTP endpoint option.
Before you install
The ATS tools create, update, move and delete records such as jobs, candidates, interviews, offers and notes, and the README describes the ATS state as in-memory, so data may not persist. The email tool sends messages through SMTP credentials passed per call, and the optional LLM scoring path takes an API key per call; keep such secrets out of shared logs. Resume files and URLs are sent to the server process, and any URL input is fetched over the network.

Installation

In SourceWeft

  1. Open Ai Hr Management Toolkit in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

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.

[image] [image] [image]

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

[npm version] [License: MIT]

[mcp-ai-hr-management-toolkit server]

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):

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

Example usage:

[image] [image]

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.


Installation Options

Option 1: NPX (Zero-install, recommended)

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

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

Option 2: Global install

Install once, use from any directory:

bash
npm install -g mcp-ai-hr-management-toolkit
json
{  "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)

bash
git clone <repo-url>cd Resume-parsernpm installnpm 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

ToolWhat it doesAI?
parse_resumeParse PDF / DOCX / TXT / MD / URL → raw text + contacts, keywords, section mapNo
batch_parse_resumesParse up to 20 files in one call, full pipeline on eachNo
inspect_pipelineRun the 5-stage analysis pipeline → confidence scores, entity counts, data quality reportNo

Unified Analysis

ToolWhat it doesAI?
analyze_resumeMaster 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 allNo

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

ToolWhat it doesAI?
assess_candidateScore against up to 8 weighted criteria axes → weighted total + pass / review / reject decisionOptional

Export & Notifications

ToolWhat it doesAI?
export_resultsExport structured parse results to JSON or CSVNo
send_emailSend results via SMTP (config passed per call — no server-side secrets stored)No

ATS — Jobs

ToolWhat it doesAI?
ats_manage_jobsFull CRUD for job postings: create, read, update, delete, list, search by title/department/statusNo

ATS — Candidates & Pipeline

ToolWhat it doesAI?
ats_manage_candidatesCRUD + analytics: add, update, move stage, bulk-move, filter, rank, compare, recommend stage changes, summarizeNo
ats_analyticsUnified dashboard + pipeline analytics: stage distribution, conversion rates, avg time-in-stage, bottleneck detection, offer acceptance rateNo
ats_searchGlobal full-text search across all ATS entities (candidates, jobs, interviews, offers, notes)No

ATS — Interviews

ToolWhat it doesAI?
ats_schedule_interviewCreate, update, and delete interviews with conflict detection and interviewer availability checkNo
ats_interview_feedbackSubmit structured feedback, compute consensus score, summarize feedback across all interviewersNo

ATS — Offers & Notes

ToolWhat it doesAI?
ats_manage_offersFull offer lifecycle: draft → pending → approved → sent → accepted / declined / expiredNo
ats_manage_notesAdd, update, search, and delete timestamped candidate notesNo

ATS — Enterprise HR

ToolWhat it doesAI?
ats_complianceEEO/EEOC reporting, GDPR export/erasure, audit trail, data retention policiesNo
ats_talent_poolPassive candidate talent pools (CRM): create pools, add/remove candidates, search, analyticsNo
ats_scorecardStructured interview scorecards with weighted criteria, per-evaluator scores, aggregate rankingsNo
ats_onboardingPost-hire onboarding checklists: tasks by category, assignees, progress tracking, overdue alertsNo
ats_communicationEmail templates with {{variable}} interpolation, send/preview, communication history, statsNo

Testing & Seeding

ToolWhat it doesAI?
ats_generate_demo_dataGenerate a realistic sample ATS dataset (jobs, candidates, interviews, offers) for testingNo

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

FormatExtensionsParser
PDF.pdfpdf-parse v2
DOCX.docxmammoth
Plain text.txtdirect read
Markdown.md, .markdownregex-based
URL / HTMLany URL stringcheerio

Max file size: 10 MB


Structured Output Schema

contact        — name, email, phone, location, LinkedIn, GitHub, website, portfoliosummary        — professional summary textskills[]       — name, category (13 types), proficiency, usage contextexperience[]   — company, title, start/end dates, highlights, achievements (with metrics), technologieseducation[]    — institution, degree, field, dates, GPAcertifications[] — name, issuer, date, credential URLprojects[]     — name, description, URL, technologies, highlightslanguages[]    — spoken language and proficiency

Web UI

The app ships with a full web interface:

TabDescription
Single ParseUpload one file or paste a URL. Returns structured data, pipeline visualization, and AI-enhanced analysis
Batch ParseUpload up to 20 files. Export to JSON / CSV / PDF or email results
ChatConversational interface with tool access — ask questions about any parsed resume
ATSFull 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:

HeaderDescription
x-api-keyYour AI provider API key
x-ai-provideropenai / anthropic / google / deepseek / glm / qwen / openrouter / opencodezen
x-ai-modelSpecific model ID
bash
# Parse a single resumecurl -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 datacurl -X POST http://localhost:3000/api/export \  -H "Content-Type: application/json" \  -d '{"format":"csv","results":[...]}'

Tech Stack

LayerTechnologies
FrameworkNext.js 16 (App Router, Turbopack), React 19, TypeScript
AIVercel AI SDK v6, multi-provider (OpenAI, Anthropic, Google, DeepSeek, GLM, Qwen, OpenRouter)
MCP@modelcontextprotocol/sdk v1.29 — Streamable HTTP + stdio transports
Parsingpdf-parse v2, mammoth, cheerio
NLPTF-IDF, NER, cosine similarity, Jaccard index (all in-process, no external services)
SchemaZod v4
ExportExcelJS (CSV/XLSX), jsPDF + jspdf-autotable
EmailNodemailer
StylingTailwind CSS v4, Framer Motion

Development

bash
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 productionnpm run build
# Test MCP with the official inspectornpx @modelcontextprotocol/inspector http://localhost:3000/api/mcpnpx @modelcontextprotocol/inspector node dist/mcp-stdio.js
# Lintnpm 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

Source: README.md at commit cbeba6c

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v3.0.5LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

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  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
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  • Documentation15 pts
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