What is Transaction Categorizer?
Transaction Categorizer is a AI tool that Processes and categorizes financial transaction data into predefined expense and income categories, outputting organized CSV files for personal finance management and expense tracking.. It has a Nerq Trust Score of 39/100 (E). 0 GitHub stars. Published by https://github.com/francesliang/custom_mcp_servers. Last analyzed September 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 0 stars, 0 downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
What Transaction Categorizer Does
Transaction Categorizer is a mcp_server in the AI tool category. Processes and categorizes financial transaction data into predefined expense and income categories, outputting organized CSV files for personal finance management and expense tracking.. It is published by https://github.com/francesliang/custom_mcp_servers and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.
Who Should Use Transaction Categorizer
Transaction Categorizer is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | https://github.com/francesliang/custom_mcp_servers |
|---|---|
| Category | AI tool |
| License | Not specified |
| Type | mcp_server |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=transaction-categorizer
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| MarkItDown | 42 | 92.8K |
| Filesystem | 50 | 89.4K |
| Time | 50 | 89.4K |
| Fetch | 50 | 89.4K |
| Sequential Thinking | 50 | 89.4K |
Frequently Asked Questions
Last updated September 2026. Trust scores based on automated analysis of public data.