Trackor▌
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
★ 3
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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.
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.
- Go to Settings → Extensions → Advanced → Install Extension…
- Select
trackor-proxy.mcpb - 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.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
- 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
- 3.Provide connection string: host, port, database, username, password
- 4.Restart Claude Desktop to load MCP server
- 5.Test connection: 'List all tables in database'
- 6.Run simple query: 'Show me 5 rows from users table'
- 7.Verify results and permissions are correct
- 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
4.6★★★★★31 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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