Portfolio CSV Analyzer MCP. Analyze massive broker exports without context window crashes
Portfolio CSV Analyzer processes massive financial exports from brokers like DEGIRO or XTB without crashing your AI client. This MCP streams data locally, preventing context window overload. It reliably extracts the full column schema and provides sample records, giving your agent a clean, safe dataset to analyze complex portfolio performance metrics.
Give Claude and any AI agent real-world access
You get an immediate list of every column header in your CSV file (e.g., Date, Product, ISIN) so your agent knows exactly what data points exist for analysis.
Your AI client can write Python or R code that sums up metrics (like total buys or dividends) using the defined column structure.
The MCP counts and reports the exact number of data rows in your entire trading history file, giving you a reliable dataset size metric.
You can ask the agent to analyze column relationships to calculate profit and loss across multiple trades based on the schema provided.
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What AI agents can do with Portfolio CSV Analyzer MCP: 1 Tool Available
Use the available tools here to process massive financial data, extract column schemas, and prepare your raw broker exports for accurate AI analysis.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Portfolio CSV Analyzer MCPParse Portfolio Csv
Reads massive CSV exports from brokers (DEGIRO, XTB, Trading212) locally to return the column schema and a data sample for safe AI analysis.
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The hassle of giant brokerage exports
Today, if you want your AI client to analyze a year's worth of trades from DEGIRO or XTB, you have to download the massive CSV file. Then you hit 'upload.' Within moments, that sheer volume of data overwhelms the model, causing context window limits. You end up with either an outright crash or an analysis that ignores huge chunks of your history.
With this MCP, you simply point it at the export file. It runs the parsing locally, extracting only the column structure and a small sample. Now your AI client gets exactly what it needs—the blueprint for your data—without ever seeing the thousands of lines of raw text.
Get accurate metrics with parse_portfolio_csv
You eliminate the manual step of opening the file, scrolling through columns, and trying to remember if 'Amount' means gross or net. The tool handles the heavy lifting of identifying every single header and data type for you.
What changes is that your agent now has a stable, verifiable foundation. It doesn't guess; it calculates based on the schema provided by parse_portfolio_csv.
What Portfolio CSV Analyzer MCP does for your AI
When you download a year's worth of trades from any major broker, the resulting CSV file is huge. If you try to upload that massive dump into your AI client, it will choke—the context window fills up, and the analysis becomes unreliable or just crashes entirely. This MCP changes that.
It acts like a local data pre-processor for your agent.
It uses a high-speed streaming parser to read your financial history line by line, all on your machine. Instead of dumping every single row into the chat, it intelligently extracts two things: the exact column headers and a small sample of the data. This gives your AI client the precise schema it needs—like knowing 'Action' or 'Value' exists—without ever overwhelming its memory.
You can then safely ask your agent to write aggregation scripts or calculate complex metrics based only on the verified structure. Because Vinkius hosts this MCP, you connect once and get access to reliable data handling for financial reporting.
This process lets you treat multi-gigabyte broker exports like a structured database query, giving you accurate insights instead of context window errors.
019e38da-2f12-7235-bff8-8b711ef0eebd How to set up Portfolio CSV Analyzer MCP
The bottom line is you get structured metadata about huge files, allowing your agent to work with massive financial datasets without running into context window limits.
Provide the absolute file path pointing to your massive CSV export from a broker (like XTB or DEGIRO).
The MCP runs a local, high-speed stream parse on the entire file, reading it line by line without sending everything to the chat.
You receive back a clean summary: the full column schema and representative sample data that your AI client can use for safe analysis.
Who uses Portfolio CSV Analyzer MCP
This MCP is for financial analysts, quantitative researchers, and portfolio managers who spend too much time wrestling raw CSV data. If you're tired of your AI client crashing every time you upload a year's worth of trades, this is what you need.
Uses the MCP to validate column structures and generate Python scripts that calculate complex risk metrics across years of transaction data.
Connects the MCP to analyze dividend streams or total cost basis by providing a clean, reliable schema derived from raw broker exports.
Employs the streaming parser to reliably process large-file financial datasets for downstream database ingestion and structured analysis.
Benefits of connecting Portfolio CSV Analyzer MCP
Avoids LLM crashes. Instead of uploading a massive file that exhausts your agent's memory, this MCP streams the data locally and only sends the necessary schema.
Guaranteed accurate structure. It automatically detects column headers and data types from complex broker exports, so you don't have to manually map fields for your AI client.
Works with huge files. The streaming parser handles CSVs of any size, making it reliable even when dealing with decades of trading history.
Safe analysis environment. By providing only the schema and a sample, you ensure that your agent is basing its calculations on verified data structure, not hallucinated context.
Directly applicable. Once you run the parse_portfolio_csv tool, your AI client can immediately guide you to write specific aggregation scripts for P&L or total dividends.
Portfolio CSV Analyzer MCP use cases
Calculating Total Dividend Income
A PM downloads a 10-year trade history CSV. Instead of uploading the whole file and hoping it doesn't crash, they run parse_portfolio_csv first. The agent gets the schema and sees 'Description' is the dividend column, allowing it to write the exact summing script needed.
Validating a New Broker Export
A Data Engineer receives an export from a new broker they haven't seen before. They use parse_portfolio_csv to immediately check for all available columns and confirm if the required 'Action' column is present, saving hours of manual data cleaning.
Auditing Transaction Counts
A Quant needs to know the exact total row count for a regulatory report. They run parse_portfolio_csv, and the tool immediately reports the precise number of transaction rows (e.g., 4,521), providing an auditable metric instantly.
Comparing Multiple Broker Exports
A PM has three different CSVs from different years/brokers. They run parse_portfolio_csv on each one to get a consistent schema for all of them, allowing the agent to compare performance metrics across disparate data sources safely.
Portfolio CSV Analyzer MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Uploading raw CSVs directly
Trying to feed a 50MB broker export straight into your AI client because you think it can 'read' the whole thing.
Run parse_portfolio_csv first. This tool reads the file locally, extracts the necessary schema and sample data, and gives your agent a manageable, safe context for deep analysis.
Guessing column names
Asking the AI to calculate 'Total Value' without knowing if the broker uses 'Value', 'Amount', or 'Net Change' in its export.
Always use parse_portfolio_csv. The tool exposes all available headers, confirming exactly what columns are present for calculations.
Relying on hallucination
The AI client giving you a partial or incorrect calculation because it ran out of memory and started guessing based on the file's start.
Use this MCP. It streams the data to prevent context overflow, ensuring your agent works only with verified column schemas derived from the full dataset.
When to use Portfolio CSV Analyzer MCP
Use this MCP if your primary pain point is processing large-scale financial CSV exports (over 10MB) that threaten to crash or overload your AI client's context window. It’s essential when you need reliable, structured metadata before running complex queries. Don't use it if you just have a small, clean spreadsheet; those files can be uploaded directly. Similarly, don't use this if you only need basic text summarizing—this tool is purely for schema and data structure extraction. You must process the file first via parse_portfolio_csv before asking your agent to perform any calculation or generate code.
Frequently asked questions about Portfolio CSV Analyzer MCP
Does Portfolio CSV Analyzer handle all broker exports? +
Yes. This MCP is designed to take massive, messy CSV files from any major brokerage (like DEGIRO or XTB) and process them reliably.
How does parse_portfolio_csv prevent context window issues? +
It uses local streaming technology. Instead of sending the entire file content to your AI client, it reads it line by line on your computer and only sends the clean schema and a sample.
Can I use this MCP for non-broker CSV files? +
The tool is optimized for financial exports. While it handles general CSV structures, its primary strength lies in reliably parsing complex trading history data.
What information do I get back after running parse_portfolio_csv? +
You receive two key things: the complete list of column headers (the schema) and a small, representative sample of the actual data rows. This gives your AI client everything it needs to start.
Is this MCP secure for private financial data? +
Yes, because the core parsing is done locally on your machine; only the structured schema and a small sample are passed to your agent, keeping the bulk of your raw data protected.