Expense Tracker MCP

A remote Model Context Protocol (MCP) server built with FastMCP that allows AI assistants such as Claude to interact with an expense-tracking system through structured tools.

Overview

This project demonstrates how an AI assistant can interact with external data and application functionality through MCP.

The Expense Tracker MCP server uses FastMCP to expose expense-management tools and SQLite to store expense data.

The server is deployed remotely using FastMCP Cloud, allowing an MCP-compatible client such as Claude to connect to it through a remote MCP endpoint.

Related MCP server: Expense Tracker MCP

Architecture

Claude
   │
   │ MCP
   ▼
Remote MCP Endpoint
   │
   ▼
FastMCP Cloud
   │
   ▼
FastMCP Expense Tracker Server
   │
   ▼
SQLite Database

Features

• Add and manage expenses

• Retrieve expense records

• Categorize expenses

• Store expense data using SQLite

• Expose expense functionality through MCP tools

• Deploy the MCP server remotely

• Connect the remote MCP server to Claude

• Allow AI assistants to interact with structured expense data

Tech Stack

• Python

• FastMCP

• Model Context Protocol (MCP)

• SQLite

• JSON

• FastMCP Cloud

• Claude

Example Interactions

Once connected to Claude, users can interact with the expense tracker using natural language.

"Add an expense of ₹500 for groceries."

"Show me my recent expenses."

"How much did I spend on food?"

"List my expenses by category."

Claude interprets the user's request and invokes the appropriate MCP tool exposed by the server.

Remote Deployment

The MCP server is deployed on FastMCP Cloud and exposed through a remote MCP endpoint.

This allows Claude and other MCP-compatible clients to access the server without running it locally.

Local Development
       ↓
FastMCP Server
       ↓
FastMCP Cloud
       ↓
Remote MCP Endpoint
       ↓
Claude

Local Setup

Clone the repository:

git clone https://github.com/khushisonwane23/expense-tracker-mcp.git
cd expense-tracker-mcp

Create a virtual environment:

python -m venv .venv

Activate the virtual environment on Windows:

.venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

If you are using uv:

uv sync

Run Locally

Run the FastMCP server:

fastmcp run server.py

The exact command may vary depending on the project configuration.

🔗 Connecting to Claude

After deploying the server to FastMCP Cloud, the application provides a remote MCP endpoint.

This endpoint can be configured in an MCP-compatible client such as Claude.

Claude
   ↓
Remote MCP Endpoint
   ↓
FastMCP Cloud
   ↓
Expense Tracker MCP Server
   ↓
SQLite Database

Once connected, Claude can discover and use the tools exposed by the MCP server.

Security

Sensitive information should never be committed to this repository.

The following files should remain private:

.env
expenses.db
.venv/
__pycache__/

API keys and other secrets should be stored using environment variables instead of being hard-coded in the source code.

Learning Goals

This project was built to understand:

• How the Model Context Protocol works

• How AI assistants interact with external tools

• How to build MCP servers using FastMCP

• How to connect LLMs with external data

• How tool-based AI workflows work

• How to deploy an MCP server remotely

• How MCP can be integrated with Claude

Future Improvements

• Add authentication and authorization

• Add monthly spending analytics

• Add budget tracking

• Add richer financial insights

• Improve error handling and input validation

• Add automated testing

• Use a production-grade database

• Add more financial management tools

Author

Khushi Sonwane

Artificial Intelligence & Robotics Student

Interested in Generative AI, AI Agents, MCP, RAG, and AI Research.