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Automated security scan — not a certification.
by JerBouma
Transparent and Efficient Financial Analysis
# Add to your Claude Code skills
git clone https://github.com/JerBouma/FinanceToolkitGuides for using mcp servers skills like FinanceToolkit.
Last scanned: 6/10/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-06-10T08:07:51.714Z",
"npmAuditRan": true,
"pipAuditRan": true
}See how FinanceToolkit compares with popular alternatives.
FinanceToolkit is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by JerBouma. Transparent and Efficient Financial Analysis. It has 5,462 GitHub stars.
Yes. FinanceToolkit passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/JerBouma/FinanceToolkit" and add it to your Claude Code skills directory (see the Installation section above).
FinanceToolkit is primarily written in Python. It is open-source under JerBouma on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh FinanceToolkit against similar tools.
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Automated security scan — not a certification.
While browsing a variety of websites, I repeatedly observed significant fluctuations in the same financial metric among different sources. Similarly, the reported financial statements often didn't line up, and there was limited information on the methodology used to calculate each metric.
For example, Microsoft's Price-to-Earnings (PE) ratio on the 6th of May, 2023 is reported to be 28.93 (Stockopedia), 32.05 (Morningstar), 32.66 (Macrotrends), 33.09 (Finance Charts), 33.66 (Y Charts), 33.67 (Wall Street Journal), 33.80 (Yahoo Finance) and 34.4 (Companies Market Cap). All of these calculations are correct, however the method of calculation varies leading to different results. Therefore, collecting data from multiple sources can lead to wrong interpretation of the results given that one source could apply a different definition than another. And that is, if that definition is even available as often the underlying methods are hidden behind a paid subscription.
This is why I designed the FinanceToolkit, this is an open-source toolkit in which all relevant financial methods (500+) are written down in the most simplistic way allowing for complete transparency of the method of calculation (proof). This enables you to avoid dependence on metrics from other providers that do not provide their methods. With a large selection of financial statements in hand, it facilitates streamlined calculations, promoting the adoption of a consistent and universally understood methods and formulas.
Beyond Equities, it supports Options, Currencies, Cryptocurrencies, ETFs, Mutual Funds, Indices, Money Markets, Commodities, Key Economic Indicators and more, allowing you to obtain historical data as well as important performance and risk measurements such as the Sharpe Ratio and Value at Risk.
Complementing this is the Finance Database 🌎, a database featuring 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets. By utilising both, it is possible to do a fully-fledged competitive analysis with the tickers found from the FinanceDatabase inputted into the FinanceToolkit.
🔌 The Finance Toolkit is also available as an MCP Server
Query 500+ methods from Claude, Copilot, Cursor, Windsurf or any MCP-compatible client without writing code.
https://financetoolkit.jeroenbouma.com/mcp — OAuth handles the rest on first use.uvx --from "financetoolkit[mcp]" financetoolkit-mcp-setup — sets up your client config and API key automatically. See MCP Server Documentation for manual setup.Also on Smithery, Glama, MCP Servers and more.
Before installation, consider starring the project on GitHub which helps others find the project as well.
To install the Finance Toolkit it simply requires the following:
pip install financetoolkit -U
Then within Python use:
from financetoolkit import Toolkit
companies = Toolkit(
tickers=['AAPL', 'MSFT'],
api_key="FINANCIAL_MODELING_PREP_KEY", # repla