databasesfinance

Trackor

at0mxploit

by at0mxploit

Trackor — simple personal expense tracking and money management using SQLite for secure, efficient budgeting and financi

Personal expense tracking and financial management through SQLite database operations.

github stars

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Remote deployment availableLocal SQLite storageStreamlit web interface included

best for

  • / Personal finance tracking and budgeting
  • / Small business expense management
  • / Financial analysis and reporting

capabilities

  • / Add expenses with dates, amounts, and categories
  • / Filter expenses by date ranges and categories
  • / Generate expense summaries and statistics
  • / Update or delete existing expense entries
  • / Export expense data to JSON or CSV
  • / Bulk delete expenses by date range

what it does

Tracks personal expenses using a SQLite database with categorization, filtering, and export capabilities. Provides comprehensive expense management through CRUD operations and financial summaries.

about

Trackor is a community-built MCP server published by at0mxploit that provides AI assistants with tools and capabilities via the Model Context Protocol. Trackor — simple personal expense tracking and money management using SQLite for secure, efficient budgeting and financi It is categorized under databases, finance. This server exposes 9 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Trackor in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.

license

MIT

Trackor is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Trackor

This is a custom MCP (Model Context Protocol) server and dumb client built with FastMCP and Streamlit.
It provides tools to track expenses, including adding, listing, summarizing, updating, and exporting data.

The server uses a local SQLite database (expenses.db) and a categories.json file for expense categories.

Tools & Resources

TOOLS (callable actions that perform operations):
- add_expense                 : Create a new expense entry
- get_expense                 : Fetch a single expense by ID
- list_expenses               : List expenses with optional filters
- update_expense              : Modify an existing expense
- delete_expense              : Remove one expense by ID
- delete_expenses_by_date_range : Remove all expenses within a date range
- summarize                   : Summarize expenses by category/subcategory
- get_statistics              : Return overall stats and monthly breakdown
- export_expenses             : Export all expenses in JSON or CSV format

RESOURCES (read-only data exposed by the server):
- expense://categories        : Provides the categories.json file (list of categories/subcategories)

Dumb MCP Client

It is Dumb MCP Client meaning without any LLM (I am poor for pro) that uses MCP Server https://at0mxploit.fastmcp.app/manifest.dxt.

Remote Deployment

It is already deployed in https://dumbclient-trackor.streamlit.app/ using Streamlit Cloud.

Local Deployment

streamlit run dumb_client/app.py

MCP Server

Remote Deployment (Easiest)

It is already deployed using FastMCP Cloud, you just need to drag this DXT File https://at0mxploit.fastmcp.app/manifest.dxt to Claude Extension. This automatically configures the server for Claude and includes all tools and resources. (Currently available only in Pro). It's setup for all different models and tools but I use Claude so.

<img width="829" height="366" alt="test" src="https://github.com/user-attachments/assets/bced55ea-eecb-4d9a-bd54-a7c44e498617" />

Local Development

Claude Connectors (remote MCP URLs) are only available for Pro users. However, non-Pro Claude Desktop users can still use this MCP server by running a local proxy.

This repository includes a proxy/ folder with a simple FastMCP STDIO bridge.

Install dependencies:

uv sync

Run MCP:

uv run main.py

Run MCP Proxy:

uv run proxy/main.py

We can also if we want use Inspector to test JSON RPC calls in MCP:

 uv run fastmcp dev .\main.py

Claude Desktop no longer auto-loads raw MCP scripts.
If you're not using Claude Pro, you must install the included desktop extension:

npm install -g @anthropic-ai/mcpb
mcpb pack proxy/ trackor-proxy.mcpb

This will generate trackor-proxy.mcpb.

  1. Go to Settings → Extensions → Advanced → Install Extension…
  2. Select trackor-proxy.mcpb
  3. Claude will load the MCP server via the local STDIO proxy.

FAQ

What is the Trackor MCP server?
Trackor is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Trackor?
This profile displays 31 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Direct Database Queries from AI

Enable Claude to query your database directly using natural language

Example

Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly

Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster

Data Analysis & Reporting

Generate complex reports and analytics without leaving conversation

Example

Analyze sales trends, cohort retention, user behavior patterns conversationally

Democratize data access—non-technical team members can query databases

Schema Exploration

Understand database structure, relationships, and data models

Example

'Explain the user_orders table schema and its relationships'

Onboard engineers faster, explore unfamiliar databases efficiently

Data Validation & Quality Checks

Run data quality queries to catch anomalies and inconsistencies

Example

Find duplicate records, missing values, orphaned foreign keys automatically

Maintain data integrity with less manual SQL work

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor with MCP support
  • Database credentials (read-only recommended for safety)
  • Network access from Claude client to database
  • Understanding of database security and access control

Time Estimate

15-30 minutes including configuration and testing

Installation Steps

  1. 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
  2. 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
  3. 3.Provide connection string: host, port, database, username, password
  4. 4.Restart Claude Desktop to load MCP server
  5. 5.Test connection: 'List all tables in database'
  6. 6.Run simple query: 'Show me 5 rows from users table'
  7. 7.Verify results and permissions are correct
  8. 8.Document query patterns for team use

Troubleshooting

  • Connection refused: Check database is running and network accessible
  • Authentication failed: Verify credentials, check user permissions
  • Claude can't see tables: Grant appropriate read permissions to database user
  • Slow queries: Add indexes, limit result set size, use read replicas
  • MCP server not loading: Check config syntax, restart Claude Desktop

Best Practices

✓ Do

  • +Use read-only database credentials to prevent accidental writes
  • +Connect to read replica, not production primary database
  • +Set query timeout limits to prevent long-running queries
  • +Document database schema and common queries for AI context
  • +Monitor query performance and optimize slow queries
  • +Use connection pooling for better performance
  • +Test with non-production data first

✗ Don't

  • Don't use production write credentials—risk of data corruption
  • Don't query production database during peak traffic hours
  • Don't expose sensitive PII without proper access controls
  • Don't skip query result validation—AI can misinterpret schema
  • Don't allow unlimited result set sizes—set LIMIT clauses
  • Don't share database credentials in plain text config files

💡 Pro Tips

  • Create database views for common queries to simplify AI access
  • Add schema comments/descriptions so AI understands column meanings
  • Use semantic table/column names ('customer_lifetime_value' not 'clv')
  • Set up query logging to audit what Claude is querying
  • Create saved query templates for recurring analysis
  • Combine with data visualization tools for better insights

Technical Details

Architecture

MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.

Protocols

  • Model Context Protocol (MCP)
  • Database-specific protocols (PostgreSQL, MySQL, MongoDB)

Compatibility

  • PostgreSQL
  • MySQL
  • SQLite
  • MongoDB
  • Redis

When to Use This

✓ Use When

Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.

✗ Avoid When

Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.

Integration

  • Read replica connection for analytics queries
  • Database view layer to abstract complex joins
  • Query result caching for repeated questions
  • Audit logging of all AI-generated queries

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.631 reviews
  • Dhruvi Jain· Dec 24, 2024

    According to our notes, Trackor benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Neel Ndlovu· Dec 16, 2024

    Trackor is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Pratham Ware· Dec 4, 2024

    We evaluated Trackor against two servers with overlapping tools; this profile had the clearer scope statement.

  • Oshnikdeep· Nov 15, 2024

    We wired Trackor into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Chinedu Menon· Nov 3, 2024

    Trackor reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Oct 6, 2024

    Trackor is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Sakshi Patil· Sep 25, 2024

    Useful MCP listing: Trackor is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Zara Lopez· Sep 21, 2024

    According to our notes, Trackor benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Hassan Yang· Sep 13, 2024

    Trackor is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Ishan Iyer· Sep 5, 2024

    Trackor has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

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