# OFX Bank Statement Parser MCP

> The OFX Bank Statement Parser takes raw, archaic bank export files (OFX/QFX) and converts them into clean, structured JSON data. It’s a secure financial bridge designed to let your AI client analyze complex transaction histories without you having to upload sensitive records to the cloud or wrestle with confusing SGML code. Your agent gets organized numbers, not raw text.

## Overview
- **Category:** data-management
- **Price:** Free
- **Tags:** financial-data, data-parsing, bank-statements, local-processing, data-extraction, transaction-history

## Description

Dealing with bank statements is usually tedious enough without needing an LLM that can't read the format. Most public AI models fail when confronted with OFX or QFX files because they use an old-school structure (SGML) that confuses standard reading algorithms. This MCP solves that problem by acting as a local financial bridge. It parses your bank export file completely on your machine, extracting only clean transactional data—Date, Amount, Description, and Type—into a structured JSON array. Your AI agent never sees the messy raw file; it only gets the organized numbers it needs to do real work. This means you can ask complex questions like 'What was my spending pattern on travel last quarter?' and get an answer without compromising your financial data. By connecting this MCP through Vinkius, you keep all your sensitive information local while giving your AI client the best possible input for accounting or budgeting.

## Tools

### parse_ofx_bank_statement
This tool takes a specified OFX or QFX file path and outputs clean, structured JSON containing all transactions.

## Prompt Examples

**Prompt:** 
```
Read my statement.ofx and categorize all my expenses into a markdown table.
```

**Response:** 
```
Here is your categorization:
| Category | Total Spent |
|---|---|
| Food & Dining | $450.00 |
| Transport | $120.00 |
```

**Prompt:** 
```
Look at my bank export and find out exactly how much I paid to 'AWS' last year.
```

**Response:** 
```
I found 12 transactions matching 'AWS' in your export, totaling exactly $1,450.32.
```

**Prompt:** 
```
Analyze my monthly income versus expenses and calculate my savings rate.
```

**Response:** 
```
Based on the OFX data, your total income was $5,000 and total expenses were $4,000. Your savings rate for this period is 20%.
```

## Capabilities

### Structure Raw Bank Files
It takes messy OFX/QFX files and converts them into clean JSON data that any program can easily read.

### Maintain Local Privacy
The parsing happens entirely on your machine, meaning zero cloud uploads of sensitive bank information.

### Extract Core Transaction Data
It isolates the key pieces of financial data—date, amount, description, and transaction type—from the noise.

## Use Cases

### Analyzing a year of spending habits
A freelance accountant needs to analyze 12 months of client expenses. Instead of manually downloading, cleaning, and uploading dozen of statements, they provide the files via this MCP and ask their agent: 'Create a pivot table showing all expense categories for Q3.' The result is an immediate markdown table without any data handling risk.

### Reconciling corporate credit cards
A finance manager needs to match raw bank exports against internal accounting software records. They run the transactions through this MCP, which spits out clean JSON, allowing their agent to easily cross-reference amounts and dates for reconciliation.

### Calculating savings rates
A personal finance user wants to know if they are meeting a savings goal. They point the tool at their bank export and prompt: 'Calculate my total income versus expenses, then determine my net saving rate for this period.' The answer is delivered instantly.

### Auditing historical transactions
An auditor needs to check all payments made to a specific vendor over several years. They use the MCP to extract data from multiple statements and ask their agent: 'List every transaction involving Vendor X, totaling the amount paid.' The clean JSON makes this query straightforward.

## Benefits

- Stop worrying about data leaks. Since the parsing happens locally on your machine, you never have to upload sensitive bank statements to a public cloud service or agent.
- Your AI client gets perfect input every time. It doesn't waste compute cycles guessing where one transaction ends and the next begins because the data is already structured JSON.
- Handle any global bank export. This MCP supports all standard OFX or QFX formats, meaning it works with practically any financial institution you use.
- Instantly answer complex questions. Instead of manually categorizing transactions in a spreadsheet, ask your agent: 'How much did I spend on dining last quarter?'
- Streamline reconciliation. By getting clean transaction data—Date, Amount, Description, Type—you speed up the matching process between bank records and internal ledgers.

## How It Works

The bottom line is your agent gets perfectly formatted numbers ready for immediate analysis, keeping your bank details private all the way through.

1. You provide the absolute file path to your OFX or QFX bank statement file.
2. This MCP runs locally, parsing the complex structure and safely extracting only clean transactional data into a structured JSON array.
3. Your AI client receives the organized JSON data, allowing it to perform analysis without ever seeing the original raw file.

## Frequently Asked Questions

**Does the OFX Bank Statement Parser handle QFX files?**
Yes, it supports both standard OFX and QFX formats. This MCP is designed to read multiple types of common bank exports so you don't have to worry about which format your bank uses.

**Is the data processed locally when using parse_ofx_bank_statement?**
Yes, that's a core feature. The parsing happens entirely on your machine; your sensitive financial details never leave your local environment and are not uploaded to any cloud service.

**What kind of data does the JSON output contain?**
The resulting JSON array contains clean, structured fields for every transaction: Date, Amount, Description, and Transaction Type. This is exactly what your agent needs to run calculations.

**Can I use this MCP with my personal bank statements?**
Absolutely. Because the process is local, it's perfect for personal finance management. You can feed it any OFX file from a major global bank and get structured data back.

**Does parse_ofx_bank_statement require me to write code?**
No, you simply provide the file path through your AI client. The MCP handles all the complex parsing logic in the background so you just get clean data ready for a prompt.