A Model Context Protocol (MCP) server for tracking job interview processes using AI-powered chat interaction.
A Model Context Protocol (MCP) server for tracking job interview processes using AI-powered chat interaction. Exposed over MCP by the job tracker ai mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
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.
Everything the assistant can do here goes through one of these:
Prerequisites — 1. Node.js installed on your system 2. A Supabase account and project 3. Your Supabase project URL and user tokenSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Job Tracker Ai's toolset — Prerequisites — is a fair guide to whether it matches your workflow. It is maintained by shayyzhakov; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Prerequisites | 1. Node.js installed on your system 2. A Supabase account and project 3. Your Supabase project URL and user token |
{
"mcpServers": {
"job-tracker": {
"command": "node",
"args": ["<path-to-job-tracker>/dist/index.js", "access-token"]
}
}
}Configuration as documented by the project. Restart the client after saving.
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