shayyzhakov/job-tracker-ai
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A Model Context Protocol (MCP) server for tracking job interview processes using AI-powered chat interaction.
LLM Chat Interface
Facilitates natural language interaction with the job tracker.
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 ā 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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ā LLM Chat āāāāā>ā MCP Tools āāāāā>ā Supabase ā
ā Interface ā ā Server ā ā Backend ā
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- 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
- Node.js installed on your system
- A Supabase account and project
- Your Supabase project URL and user token
Setup in Your AI Development Environment
- 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