# Transaction Categorizer

> Use this tool when you need to organize and make sense of financial transaction data. It categorizes transactions into predefined expense and income categories, solving problems related to personal finance management, expense tracking, and financial data organization. The tool takes transaction files as input and outputs categorized transactions in CSV format, making it ideal for use in AI assistant conversations that involve financial planning and analysis.

Canonical page: https://skillsregistry.net/skills/francesliang-transaction-categorizer  
JSON: https://api.skillsregistry.net/v1/skills/francesliang-transaction-categorizer

## Description

The Transaction Categoriser MCP server provides AI assistants with the ability to process and organize financial transaction data. Built with Python using the FastMCP framework, it reads transaction files from a specified folder and categorizes them into predefined expense categories (such as Food/drinks, Transportation, Utilities) and income categories (Paycheck, Rental income, Other). The server outputs the categorized transactions in CSV format with date, amount, description, and category fields, making it particularly useful for personal finance management, expense tracking, and financial data organization within AI assistant conversations.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** finance
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/francesliang-transaction-categorizer)
- **Repository:** <https://github.com/francesliang/custom_mcp_servers>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "francesliang-transaction-categorizer"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/francesliang-transaction-categorizer` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/francesliang-transaction-categorizer/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt