
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
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.
Installation
In SourceWeft
- Open Ai Hr Management Toolkit in the dashboard and add it to a workspace.
- 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
[image] [image] [image]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
[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):
Example usage:
[image] [image]Cursor — Add to .cursor/mcp.json in your project root:
VS Code Copilot — Create .vscode/mcp.json in your project root:
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):
Option 2: Global install
Install once, use from any directory:
Option 3: Remote HTTP endpoint
Deploy the Next.js app and use the Streamable HTTP transport:
Test locally:
Option 4: Local development (Web UI + MCP)
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
Unified Analysis
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
Export & Notifications
ATS — Jobs
ATS — Candidates & Pipeline
ATS — Interviews
ATS — Offers & Notes
ATS — Enterprise HR
Testing & Seeding
assess_candidateoptionally calls an LLM when you supplyprovider+apiKey; it falls back to fully algorithmic scoring otherwise.
Example Multi-Turn Flow
Analysis Pipeline
Every resume runs through a 5-stage algorithmic pipeline:
- 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
Max file size: 10 MB
Structured Output Schema
Web UI
The app ships with a full web interface:
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:
Tech Stack
Development
Project Structure
License
Source: README.md at commit cbeba6c
Tools
0Version history
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
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts