# SEC EDGAR Financials MCP for AI Agents AI Agent Connect > SEC EDGAR Financials MCP gives you instant access to audited financial data for any U.S. public company. It pulls XBRL facts like revenue, net income, assets, and EPS directly from SEC filings. Use it to build financial models, run industry-wide comparisons, or grab specific US-GAAP metrics without ever opening a PDF or a browser tab. It's like having a Bloomberg terminal for free. ## Overview - **Category:** data-intelligence - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_2h2NgYUbwlMmbq7EatEwPKwrMgOgduayGu5PZADP/ai-agent-connect - **Tags:** financial-analysis, xbrl, financial-metrics, market-data, corporate-finance, industry-benchmarking, accounting-standards, data-extraction ## Description This Connector lets you pull audited financial data from SEC filings into your AI client's workspace. Instead of hunting through 10-Ks or trying to parse messy PDFs, you can ask your agent to grab the latest revenue numbers or a full dump of a company's XBRL facts. It handles the heavy lifting of navigating the SEC's database so you can focus on the analysis. You can pull specific US-GAAP concepts like R&D expenses or debt levels for any public company. You can also pull industry-wide data to see how a company stacks up against its competitors on a specific metric for a specific year. It's a huge time saver for anyone who needs accurate, standardized data. Since Vinkius hosts this, you can plug it into your existing workflow and start pulling data immediately without worrying about authentication. It turns your AI into a research powerhouse that knows the numbers as well as a seasoned analyst. You no longer have to worry about the technical hurdles of XBRL frames or manual data entry. You get clean, structured results that you can use to build models, write reports, or perform deep-dive due diligence on any U.S. public company. This means you can move from a research question to a structured data table in seconds. Whether you are looking for a single line item or a massive data dump, the information is always pulled directly from the source. You get to spend your time thinking about what the numbers mean rather than where to find them. ## Tools ### get_key_financials This tool provides a summary of the last 5 reported values for revenue, assets, and EPS. It gives you a quick snapshot of a company's health. ### get_financial_metric This tool pulls a specific US-GAAP concept like R&D or debt for any public company. It helps you find exact line items quickly. ### get_all_company_facts This tool pulls a raw, complete XBRL data dump for a company's entire history. Use it for deep-dive historical analysis. ### get_industry_comparison This tool compares a specific financial metric across every company in an industry. It is perfect for rapid benchmarking. ## Prompt Examples **Prompt:** ``` What was Amazon's revenue for the last 5 quarters? ``` **Response:** ``` ### Amazon.com, Inc. (AMZN) **Revenue (Last 5 Reported Quarters):** | Quarter | Revenue | | :--- | :--- | | Q4 2024 | $188.89B | | Q3 2024 | $259.01B | | Q2 2024 | $244.91B | | Q1 2024 | $143.88B | | Q4 2023 | $153.62B | ``` **Prompt:** ``` Give me a list of all companies' R&D expenses for 2024. ``` **Response:** ``` ### Industry Frame: ResearchAndDevelopmentExpense (CY2024) Here are the reported R&D expenses for top companies in the industry for 2024: * **Microsoft:** $28.1B * **Alphabet:** $45.4B * **Meta:** $39.1B * **NVIDIA:** $8.6B ``` **Prompt:** ``` What is Tesla's net income and total assets? ``` **Response:** ``` ### Tesla, Inc. (TSLA) **Key Financials:** * **Net Income:** $14.99B * **Total Assets:** $106.60B *Data pulled from most recent 10-K/10-Q filings.* ``` ## Capabilities ### Pull latest revenue and net income figures Get the most recent reported values for a company's top-line and bottom-line results. ### Extract specific US-GAAP accounting concepts Retrieve specific line items like R&D spend, debt levels, or common stock shares. ### Download full XBRL financial data dumps Access the complete dataset of financial facts for a company across multiple years. ### Compare company metrics across entire industries Pull a specific financial metric for every company in an industry to see how they stack up. ### Get multi-year historical financial values Retrieve a history of reported values for key metrics like assets and equity. ## Use Cases ### Comparing tech giants An analyst wants to see the revenue of all tech companies in 2024. They ask the agent to use `get_industry_comparison` to pull the data. ### Deep dive on a specific company A researcher needs to find Meta's exact R&D spend. The agent uses `get_financial_metric` to find the correct US-GAAP concept. ### Quick portfolio overview A user wants a snapshot of Apple's last 5 quarters. The agent pulls the data via `get_key_financials` for a fast summary. ### Training a model A data scientist needs every fact for a company's history. They use `get_all_company_facts` to get the full XBRL dump. ## Benefits - Stop manual PDF scraping with `get_key_financials` to get a summary of the last 5 reported values instantly. - Build better models by pulling specific US-GAAP concepts like R&D or debt using `get_financial_metric`. - Run fast competitive analysis by comparing metrics across entire industries with `get_industry_comparison`. - Access deep historical data for complex research using the full XBRL data dump from `get_all_company_facts`. - Save hours of data entry by letting your AI client handle the SEC's complex XBRL frames for you. - Get standardized data for any U.S. public company without having to navigate the SEC website yourself. ## How It Works The bottom line is you get instant, accurate financial data without the manual research. 1. Connect the Connector to your AI client via Vinkius. 2. Ask your agent for a specific company's financials or an industry comparison. 3. Receive structured data ready for analysis or model building. ## Frequently Asked Questions **Can I use the SEC EDGAR Financials MCP to find revenue for any US public company?** Yes, this Connector allows your AI client to pull revenue and other key financial facts for any public company listed on the U.S. Securities and Exchange Commission (SEC) database. **Does the SEC EDGAR Financials MCP provide data for private companies?** No, this Connector specifically extracts data from SEC filings, which are only available for public companies. It cannot access private company financials. **Can the SEC EDGAR Financials MCP help me compare companies in the same industry?** Yes, it includes a specific tool for industry comparisons. You can request a specific metric, like R&D spend, for all companies in a specific industry frame. **What specific financial metrics can I pull using the SEC EDGAR Financials MCP?** You can pull a wide range of US-GAAP concepts, including revenue, net income, total assets, liabilities, stockholders' equity, EPS, and cash equivalents. **Does the SEC EDGAR Financials MCP include real-time stock prices or live trading data?** No, this Connector provides audited financial facts from official filings. It does not provide real-time stock quotes, live price movements, or trading data. **How accurate is the data I get from the SEC EDGAR Financials MCP?** The data is pulled directly from the official SEC XBRL filings, which are the audited financial statements provided by the companies to the government. **What is XBRL?** XBRL (eXtensible Business Reporting Language) is a standardized format for financial data required by the SEC since 2009. It tags every financial number (revenue, assets, debt, etc.) with a machine-readable label, making it possible to extract and compare financial data across companies automatically. **What is a US-GAAP concept?** US-GAAP comprises the standard accounting principles in the US. Each accounting term (like 'Revenues' or 'NetIncomeLoss') maps directly to specific facts filed in XBRL format. **What are frames?** The SEC provides 'Frames' to view an entire industry's metric at once (e.g., all revenues in Q1 2024) instead of polling company-by-company.