Which integrations are available for this server?

Utilizes Google's Gemini AI to enable a natural language interface for expense management, subscription analysis, and intelligent transaction categorization. Integrates with Supabase to provide persistent storage for expenses, hierarchical categories, and transaction tags, utilizing pgvector for similarity-based categorization and JWT for authentication.

How do I use Expense Tracker MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Expense Tracker MCP I spent $45 on dinner at Olive Garden last night" 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.

Expense Tracker Backend

AI-powered expense tracking system with natural language interface, intelligent categorization, and real-time sync.

Architecture

The system uses a two-server architecture:

MCP Server: Core expense tracking tools exposed via Model Context Protocol
Gemini AI Server: FastAPI server providing chat interface with authentication

Features

🤖 Natural language expense management via Gemini AI
🧠 Intelligent categorization using embeddings and similarity search
🔐 JWT authentication with Supabase
📊 Hierarchical categories for organization
🏷️ Predefined tag system
📈 Real-time data sync
🔄 Learning system that improves over time

Quick Start

Prerequisites

python3 -m venv venv source venv/bin/activate pip install -r requirements.txt

Environment Setup

cp .env.example .env # Add your credentials: # - SUPABASE_URL # - SUPABASE_KEY # - GOOGLE_API_KEY (for Gemini)

Database Setup

Execute the SQL scripts in your Supabase SQL Editor:

# Core tables scripts/create_tables.sql # Embeddings support scripts/create_embeddings_schema.sql

Run Both Servers

Terminal 1 - MCP Server:

python run_mcp.py

Terminal 2 - Gemini AI Server:

uvicorn app.servers.gemini.main:app --reload --port 8000

Initialize Data

# Populate categories python scripts/populate_hierarchical_categories.py # Populate predefined tags python scripts/populate_predefined_tags.py

API Endpoints

Chat Interface

POST /chat - Send natural language commands
POST /auth/refresh - Refresh JWT token

MCP Tools (via chat)

Create expenses from natural language
Auto-categorize transactions
Get spending summaries
Analyze subscriptions
View recent transactions

Flutter Client

refer https://github.com/keyurgit45/expense-tracker-client

Testing

# Run all tests with mocks ENVIRONMENT=test pytest tests/ -v # Run specific components ENVIRONMENT=test pytest tests/test_mcp_tools.py -v ENVIRONMENT=test pytest tests/test_categorization.py -v

Project Structure

backend/ ├── app/ │ ├── core/ # Business logic │ ├── servers/ │ │ ├── gemini/ # AI chat server │ │ └── mcp/ # MCP tool server │ └── shared/ # Shared configs ├── scripts/ # Utilities └── tests/ # Test suite

AI Categorization

The system uses a hybrid approach:

Generates embeddings for transactions using Sentence Transformers
Finds similar past transactions using pgvector
Uses weighted voting to predict categories
Falls back to rule-based matching
Learns from user confirmations

Development

API docs: http://localhost:8000/docs
Frontend integration: Configure CORS in Gemini server
MCP tools can be tested directly via chat interface

This server cannot be installed

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security - not tested

F

license - not found

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quality - not tested

How are these scores calculated?

Resources

GitHub Repository

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