job-tracker-ai

shayyzhakov/job-tracker-ai

3.2

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A Model Context Protocol (MCP) server for tracking job interview processes using AI-powered chat interaction.

Tools
  1. LLM Chat Interface

    Facilitates natural language interaction with the job tracker.

  2. MCP Tools Server

    Exposes structured endpoints for LLM interaction.

Job Tracker MCP Server

A Model Context Protocol (MCP) server for tracking job interview processes using AI-powered chat interaction.

Overview

This service exposes structured tools (via MCP) that enable users to log, update, and query their ongoing job applications, interviews, contacts, and outcomes — all through natural language conversations with an LLM. Backed by Supabase for fast prototyping and persistent storage, it's designed to work seamlessly with LLMs like GPT Claude.

Features

  • Structured Event Tracking
    • Applications
    • Interviews
    • Offers
    • Follow-ups
  • Company & Role Management
    • Company profiles
    • Role details and requirements
  • Data Management
    • Compensation tracking
    • Contact history
    • Application status updates
  • AI-Powered Assistance
    • Context-aware Q&A
    • Natural language interaction
    • Intelligent insights

Design

The Job Tracker is built with a modern, scalable architecture:

Backend Infrastructure

  • Supabase Backend
    • PostgreSQL database for robust data storage
    • Row Level Security (RLS) for data privacy
    • Built-in authentication and user management

System Components

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│   LLM Chat   │────>│   MCP Tools  │────>│   Supabase   │
│  Interface   │     │   Server     │     │   Backend    │
ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜     ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜     ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜
  • MCP Tools Layer: Exposes structured endpoints for LLM interaction
  • Data Models:
    • Companies
    • Roles (applications)
    • Interview Events
    • Contacts

The system leverages Supabase's serverless architecture, eliminating the need for traditional backend maintenance while providing enterprise-grade reliability and security.

How To Use

Prerequisites

  1. Node.js installed on your system
  2. A Supabase account and project
  3. Your Supabase project URL and user token

Setup in Your AI Development Environment

  1. Add the following configuration to your AI agent's MCP servers configuration:
{
  "mcpServers": {
    "job-tracker": {
      "command": "node",
      "args": ["<path-to-job-tracker>/dist/index.js", "access-token"]
    }
  }
}

Replace the placeholders:

  • <path-to-job-tracker>: Path to the installed job-tracker-mcp directory
  • <access-token>: Your access token

Available Commands

Once configured, you can interact with the job tracker through natural language in your AI chat. Examples:

  • "Add a new company I'm applying to"
  • "Log a new interview for [company]"
  • "Update the status of my application at [company]"
  • "Show me all my upcoming interviews"
  • "List all companies I've applied to"

The AI will automatically use the appropriate MCP tools to manage your job search data.

Printing Logs

The log file is written to mcp-tool.log in your user's home directory. To view the application's logs in real-time, you can use the following command in your terminal:

tail -f ~/.config/job-tracker-mcp/mcp-tool.log