Job hunting is messy. Emails pile up, spreadsheets get forgotten, and you lose track of which companies you've contacted. Bapiraj from Hundred Folds shows how to build a job application tracking system using Model Context Protocol (MCP) that lets you ask Claude questions like "What's my application status breakdown for last month?" and get instant answers.
What This MCP Server Does
This isn't just another CRUD demo. The job platform MCP server creates a complete hiring ecosystem where both candidates and recruiters can interact through natural language. You can search job postings, submit applications, track status changes, and even create new job listings - all by talking to Claude.
The system uses SQLite as the database backend with Python handling the server logic. FastMCP makes it easy to expose Python functions as MCP tools that Claude can call directly. No complex API wrappers or authentication layers needed.
Key Features Built
The video walks through implementing several core tools. For job seekers, there's job search functionality that filters by location, salary, and other criteria. The application system tracks your submissions and lets you monitor status updates as companies move you through their hiring pipeline.
Recruiters get their own set of tools. They can post new positions, view all applicants for specific roles, and update candidate statuses from "applied" to "interview" to "hired" or "rejected." Everything flows through the same MCP interface that Claude understands.
What makes this interesting is how the database interactions happen through conversational prompts. Instead of writing SQL queries or navigating admin panels, you just ask Claude to "show me all software engineering jobs in San Francisco" or "update John's application status to interview scheduled."
Implementation Details
The technical setup is straightforward. FastMCP handles the MCP protocol implementation, so you focus on writing the actual business logic. Each tool function takes parameters that Claude extracts from your natural language requests.
The SQLite schema includes tables for jobs, applications, and users. The Python functions handle database operations and return structured data that Claude can present in readable formats. Error handling ensures the system gracefully deals with invalid job IDs or duplicate applications.
Connecting to Claude Desktop requires updating your MCP configuration file to point to the local server. Once connected, Claude gets immediate access to all the job platform tools.
Why This Matters for MCP Development
This project demonstrates MCP's strength for database-driven applications. Instead of building separate web interfaces for different user types, you get one conversational interface that handles everything. The same system works for candidates checking their application status and recruiters managing their hiring pipeline.
The pattern here applies beyond job hunting. Any workflow with multiple actors and complex state tracking could benefit from this approach. Project management, customer support, inventory tracking - they all have similar characteristics.
Getting Started
The complete code is available on GitHub, including the SQLite schema and sample data. The video provides step-by-step setup instructions for running the MCP server locally and connecting it to Claude Desktop.
If you're building MCP servers that need persistent data storage, this project offers a solid foundation. The combination of SQLite, Python, and FastMCP creates a development stack that's both simple to understand and powerful enough for real applications.
Source: Job Applications Are Chaos — Let's Use AI to Manage Them | MCP Playbook EP 2