Expense Tracker MCP

An AI-powered Expense Tracker built using FastMCP and SQLite. This project exposes expense management capabilities as MCP tools, allowing AI assistants such as Claude Desktop to interact with a persistent expense database through the Model Context Protocol (MCP).

Features

• Add new expenses

• List expenses within a date range

• Update existing expenses

• Delete expenses

• Summarize expenses by category

• Expose expense categories as an MCP Resource

• Persistent local storage using SQLite

Related MCP server: ExpenseTracker MCP Server

Tech Stack

• Python 3.11+

• FastMCP

• SQLite

• JSON

• MCP (Model Context Protocol)

Project Structure

Expense-Tracker-MCP/
│
├── main.py               # FastMCP server
├── database.py           # Database operations
├── categories.json       # Expense categories
├── expenses.db           # SQLite database (generated at runtime)
├── pyproject.toml
├── uv.lock
└── README.md

MCP Tools

MCP Resources

expense://categories

Returns the list of supported expense categories and subcategories from categories.json.

Installation

Clone the repository:

git clone https://github.com/navalmishra/Expense-Tracker-MCP.git
cd Expense-Tracker-MCP

Install dependencies:

uv sync

or

pip install -e .

Running the Server

Start the FastMCP server:

python main.py

The server starts at:

http://localhost:8000/mcp

Connecting with Claude Desktop

Add the server to your Claude Desktop configuration:

{
  "mcpServers": {
    "expense-tracker": {
      "command": "python",
      "args": [
        "/path/to/main.py"
      ]
    }
  }
}

Restart Claude Desktop, and the Expense Tracker tools will be available automatically.

Example Workflow

Ask Claude:

Add an expense of ₹500 for groceries today.

Claude calls:

add_expense(...)

Ask:

Show my expenses for this month.

Claude calls:

list_expenses(...)

Ask:

Summarize my food expenses.

Claude calls:

summarize_expenses(...)

Database

The application uses SQLite for local persistence.

The database is created automatically on the first run if it does not already exist.

Future Improvements

• Category validation using categories.json

• Pydantic models for input validation

• Monthly and yearly reports

• Expense analytics

• Budget tracking

• Cloud database support (PostgreSQL/Turso)

• Authentication

• Deployment on FastMCP Cloud

Learning Objectives

This project demonstrates:

• Building custom MCP servers

• Creating MCP Tools and Resources

• Integrating SQLite with FastMCP

• Persistent AI applications

• Connecting custom MCP servers with Claude Desktop

• Deploying MCP servers to FastMCP Cloud

License

This project is licensed under the MIT License.