Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Expense Tracker MCP Add a $45.75 lunch expense to Dining and show my balance"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A local expense and income tracker backed by SQLite. The project provides a Model Context Protocol (MCP) server for AI assistants (such as Claude Desktop, Cursor, and other MCP clients) and an optional FastAPI REST server for HTTP clients.
Expenses: Add, list, edit, and delete expense records.
Income & Credits: Store income/credit entries and calculate your current balance in real time.
Categorization: Summarize spending grouped by category with optional date and category filters.
Category Catalog: Load and query category/subcategory definitions from categories.json.
Local Storage: All records are stored locally in SQLite (expenses.db), ensuring privacy.
Related MCP server: ExpenseTracker MCP Server
Python: 3.13 or newer
FastMCP: High-performance MCP server implementation
SQLite: Local relational storage
FastAPI & Uvicorn: Optional REST API with OpenAPI documentation
uv: Fast Python package and dependency manager
.
|-- expense_tracker.py # Main FastMCP expense tracker server
|-- server.py # Optional FastAPI REST API server
|-- categories.json # Expense categories and subcategories catalog
|-- pyproject.toml # Project metadata and dependencies (uv)
|-- uv.lock # Locked Python dependencies
|-- expenses.db # Local runtime SQLite database (git-ignored)
`-- src/expense_tracker_mcp/ # Package entry point
`-- __init__.pyInstall uv if not already installed.
Clone or navigate to this directory and sync dependencies:
# Sync core MCP dependencies
uv sync
# (Optional) Include FastAPI REST dependencies
uv sync --extra apiThe database (expenses.db) and its tables are automatically created on first run:
expenses: id, date, amount, category, subcategory, note
credit: id, date, amount, source, note
Start the MCP server using either of the following commands:
uv run python expense_tracker.pyOr via the installed package command:
uv run expense-tracker-mcpTool | Parameters | Description |
|
| Add an expense (date defaults to today). |
|
| List expense records with optional date range and limit. |
|
| Group expenses by category and calculate totals. |
|
| Update fields of an existing expense record. |
|
| Delete an expense by ID. |
|
| Add an income or credit record. |
|
| List recent credit entries. |
| (none) | Return total credits, total expenses, and current balance. |
Resource URI | MIME Type | Description |
|
| Provides the category catalog from |
To connect this server to Claude Desktop, add the configuration below to your claude_desktop_config.json:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"expense-tracker": {
"command": "uv",
"args": [
"run",
"--directory",
"C:\\Users\\Dell\\OneDrive\\Desktop\\Expense_tracker_mcp",
"python",
"expense_tracker.py"
]
}
}
}(Replace the path with the absolute path to your project directory).
server.py provides equivalent HTTP endpoints and interactive OpenAPI documentation.
Start the API:
uv run python server.pyAPI Base URL: http://localhost:8000
Interactive Swagger UI: http://localhost:8000/docs
Method | Endpoint | Description |
|
| Add a new expense |
|
| List expenses (supports |
|
| Update an existing expense |
|
| Delete an expense |
|
| Get expense summary by category |
|
| Add a credit / income entry |
|
| List credit entries |
|
| Get current balance |
|
| Retrieve categories catalog |
curl -X POST http://localhost:8000/expenses \
-H "Content-Type: application/json" \
-d '{
"amount": 25.50,
"category": "food",
"subcategory": "dining_out",
"note": "Lunch"
}'All financial records are stored locally in expenses.db within the project root. This file is excluded by .gitignore to prevent sensitive financial data from being committed to version control.
Add credit/income entry.
Inputs: date, note, amount, source.
Add a new expense entry to the database.
Inputs: date, note, amount, category, +1 more.
Delete an expense entry.
Inputs: expense_id (required).
Edit an existing expense.
Inputs: expense_id (required), date, note, amount, +2 more.
Get current balance (total credits - total expenses).
Inputs: no named parameters.
List credit entries.
Inputs: limit.
List expense entries. Optionally filter by date range.
Inputs: limit, end_date, start_date.
Summarize expenses by category. Optionally filter by date range or category.
Inputs: category, end_date, start_date.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 8 tools
Each tool targets a clearly distinct operation: add/list/edit/delete expenses, add/list credits, summarize, and get balance. There are no overlapping or confusable tools; an agent can easily select the correct action.
Most tools follow a consistent verb_noun pattern like add_expense, list_expenses, edit_expense, delete_expense, add_credit. Minor deviations include the bare verb 'summarize' and 'get_balance' instead of something like 'summarize_expenses' or 'get_balance', but the overall pattern remains readable and predictable.
With 8 tools, the set is well-scoped for an expense tracker. There are enough tools to cover core operations without unnecessary redundancy or overwhelming the agent.
Expenses have full CRUD coverage (add, list, edit, delete) and there is summarizing plus balance calculation. However, credits only support add and list, with no edit_credit or delete_credit, which creates a notable gap in managing income entries and could leave incorrect credits uncorrectable.
Log, query, and edit expenses, budgets, and accounts in Manilo from any MCP-compatible AI assistant.
Log, query, and edit expenses, budgets, and accounts in Manilo (formerly Ledgy) from any MCP-compatible AI assistant.
Log, query, and edit expenses, budgets, and accounts in Ledgy from any MCP-compatible AI assistant.
Personal finance for AI agents — onboard, import statements, categorize & budget over MCP.
