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Personal Expense Manager

by Jiliar
GitHub
Python
  • Linux
  • Apple
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📊 MCP Server - Personal Expense Manager

An MCP (Model Context Protocol) server for managing and analyzing personal expenses from CSV files.

🚀 Features

  • Add expenses to a CSV file with categorization

  • Get recent expenses with day-based filtering

  • MCP Resource for direct access to all expenses

  • Specialized prompt that generates automatic analytical summaries

  • Analysis by category and payment method

  • Trend detection and spending patterns

📋 Requirements

pip install fastmcp mcp

🗂️ Project Structure

expenses-mcp-server/ ├── server.py # Main MCP server ├── data/ │ └── expenses.csv # Expenses file (created automatically) ├── requirements.txt └── README.md

🛠️ Installation & Usage

1. Clone or create the project

mkdir expenses-mcp-server cd expenses-mcp-server

2. Create virtual environment (recommended)

python -m venv venv source venv/bin/activate # Linux/Mac # or venv\Scripts\activate # Windows

3. Install dependencies

pip install fastmcp mcp

4. Run the server

python server.py

🔧 Available Functionalities

Tools

1. agregar_gasto (Add Expense)

Adds a new expense to the system.

Parameters:

  • fecha: Date in 'YYYY-MM-DD' format

  • categoria: Expense category (e.g., "Food", "Transport")

  • cantidad: Expense amount (float)

  • metodo_pago: Payment method used

Example:

agregar_gasto("2024-01-15", "Groceries", 150.75, "Debit Card")

2. obtener_gastos_recientes (Get Recent Expenses)

Gets expenses from the last N days.

Parameters:

  • dias: Number of days to query (default: 5)

Example:

obtener_gastos_recientes(7) # Last 7 days

Resource

resource://gastos

Direct access to all stored expenses.

Specialized Prompt

Resumen de Gastos Recientes (Recent Expenses Summary)

Generates a prompt with structured data for AI to create a complete analysis including:

  • 📈 Statistical calculations (totals, averages)

  • 🏷️ Category analysis

  • 💳 Payment method distribution

  • 🔍 Trend identification

  • 💡 Personalized recommendations

📊 CSV Structure

The data/expenses.csv file has the following structure:

fecha,categoria,cantidad,metodo_pago 2024-01-15,Groceries,150.75,Debit Card 2024-01-16,Transport,45.50,Cash 2024-01-17,Entertainment,89.99,Credit Card

🔌 MCP Client Integration

Python Client Example

import asyncio from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client async def main(): server_params = StdioServerParameters( command="python", args=["server.py"] ) async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: # Initialize session await session.initialize() # Add expense result = await session.call_tool( "agregar_gasto", { "fecha": "2024-01-18", "categoria": "Restaurant", "cantidad": 85.50, "metodo_pago": "Credit Card" } ) print(result) # Get recent expenses expenses = await session.call_tool( "obtener_gastos_recientes", {"dias": 5} ) print(expenses) if __name__ == "__main__": asyncio.run(main())

🎯 Use Cases

1. Daily Expense Tracking

# Add transport expense agregar_gasto("2024-01-18", "Transport", 35.00, "Cash") # Add food expense agregar_gasto("2024-01-18", "Food", 120.00, "Debit Card")

2. Weekly Analysis

# Get last week summary obtener_gastos_recientes(7)

3. Monthly Report

# Complete 30-day analysis obtener_gastos_recientes(30)

📈 Example Generated Analysis

The specialized prompt generates analysis like:

📊 EXPENSE SUMMARY - LAST 5 DAYS 💰 TOTAL SPENT: $1,245.75 📅 DAILY AVERAGE: $249.15 🔢 TRANSACTIONS: 8 purchases 🏷️ CATEGORY DISTRIBUTION: • Groceries: 45% ($560.25) • Transport: 25% ($311.44) • Entertainment: 20% ($249.15) • Restaurant: 10% ($124.58) 💳 PAYMENT METHODS: • Credit Card: 60% • Debit Card: 30% • Cash: 10% 📈 OBSERVATIONS: • Highest spending on Wednesday ($420.50) • "Groceries" category represents almost half of expenses • Growing trend in credit card usage 💡 RECOMMENDATIONS: • Consider bulk purchases to reduce grocery expenses • Diversify payment methods for better control • Set weekly limit for entertainment

🛠️ Troubleshooting

Error: "FileNotFoundError"

  • Ensure the data/ directory exists

  • Server creates the file automatically with the first expense

Error: "Encoding issues"

  • Server uses UTF-8 for special character compatibility

Error: "Invalid date format"

  • Use exact format: YYYY-MM-DD

  • Example: 2024-01-18

📝 License

MIT License

📞 Support

For issues and questions, open a ticket in the project repository.

Start tracking your expenses intelligently! 🚀

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Enables tracking and analyzing personal expenses stored in CSV files. Supports adding expenses with categorization, retrieving recent spending data, and generating automated analytical summaries with spending patterns and recommendations.

  1. 🚀 Features
    1. 📋 Requirements
      1. 🗂️ Project Structure
        1. 🛠️ Installation & Usage
          1. 1. Clone or create the project
          2. 2. Create virtual environment (recommended)
          3. 3. Install dependencies
          4. 4. Run the server
        2. 🔧 Available Functionalities
          1. Tools
          2. Resource
          3. Specialized Prompt
        3. 📊 CSV Structure
          1. 🔌 MCP Client Integration
            1. Python Client Example
          2. 🎯 Use Cases
            1. 1. Daily Expense Tracking
            2. 2. Weekly Analysis
            3. 3. Monthly Report
          3. 📈 Example Generated Analysis
            1. 🛠️ Troubleshooting
              1. Error: "FileNotFoundError"
              2. Error: "Encoding issues"
              3. Error: "Invalid date format"
            2. 📝 License
              1. 📞 Support

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