Expense Tracker MCP Server

A lightweight, async Model Context Protocol (MCP) server that lets any MCP-compatible AI assistant (like Claude) add, list, and summarize your personal expenses — backed by SQLite.

Built with FastMCP and deployed on FastMCP Cloud.

🔗 Live MCP endpoint: https://slippery-tomato-sawfish.fastmcp.app/mcp

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What it does

Once connected, an AI assistant can manage your expenses conversationally — no forms, no spreadsheets. Just say "add ₹500 for groceries" or "summarize my July spending" and it happens.

Tools

Tool	Description
add_expense(date, amount, category, subcategory="", note="")	Adds a new expense entry to the database
list_expenses(start_date, end_date)	Lists all expenses within an inclusive date range, most recent first
summarize(start_date, end_date, category=None)	Groups and totals expenses by category within a date range

Resources

• expense:///categories — Returns the list of supported expense categories as JSON.

Tech Stack

• FastMCP — MCP server framework
• aiosqlite — fully async SQLite driver (no blocking calls on the event loop)
• SQLite — simple, file-based storage (auto-initialized on first run)

Why async?

Every tool (add_expense, list_expenses, summarize) is defined with async def and uses aiosqlite under the hood. This means the server doesn't block while waiting on database I/O — multiple requests can be handled concurrently instead of queuing behind a single synchronous call.

Database

The database (expenses.db) is created automatically in the system's temp directory on first run, with the following schema:

CREATE TABLE expenses(
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    date TEXT NOT NULL,
    amount REAL NOT NULL,
    category TEXT NOT NULL,
    subcategory TEXT DEFAULT '',
    note TEXT DEFAULT ''
)

WAL journal mode is enabled for better concurrent read/write performance.

Running Locally

pip install fastmcp aiosqlite

python server.py

By default this starts an HTTP server on 0.0.0.0:8000. To run over stdio instead (for local MCP clients), swap the mcp.run(...) call at the bottom of the file.

Deploying

This server is deployed on FastMCP Cloud, which handles hosting the HTTP transport and gives you a public MCP URL you can plug into any MCP client (Claude, etc.) — no need to manage your own server or ngrok tunnel.

Connecting from Claude

Add the MCP endpoint as a connector:

https://slippery-tomato-sawfish.fastmcp.app/mcp

Once connected, just talk to it naturally:

"Add an expense of ₹450 for groceries today" "Show me my expenses from this month" "Summarize my spending by category"

Roadmap / Ideas

• [ ] Edit/delete expense entries
• [ ] Monthly budget limits + alerts
• [ ] Export to CSV
• [ ] Multi-user support with auth

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Built as a hands-on project to learn the Model Context Protocol — from local server to a deployed, always-on tool an AI assistant can actually use.