Since 1934, the SEC has required public companies to disclose financial information, creating transparency for investors. The EDGAR system, launched in the 1990s, made these filings freely available online. However, a gap remains: institutional investors have sophisticated tools to analyze this data, while individual investors face complex documents and technical barriers. SEC EDGAR MCP bridges this gap by making financial analysis accessible through natural language.

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The Problem

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The Vision

Make SEC data accessible to everyoneFinancial data should be available to researchers, analysts, developers, and anyone interested in understanding public companies. SEC EDGAR MCP removes technical barriers and provides a simple, natural language interface.
Instead of: Complex API calls and data parsing
Now: "What was Tesla's revenue growth last quarter?"

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Real-World Impact

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For Financial Analysts

1

Before SEC EDGAR MCP

Hours of manual work
  • Navigate SEC EDGAR website manually
  • Download filings individually
  • Parse documents for relevant sections
  • Extract data into spreadsheets
  • Calculate metrics manually
2

After SEC EDGAR MCP

Natural language queries
"Compare Apple and Microsoft's R&D spending as a percentage of revenue over the last 3 years"
The AI assistant handles all the complexity behind the scenes.

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For Researchers

Academic researchers can now focus on analysis rather than data collection and cleaning.
Research Examples:
  • “Analyze the relationship between CEO compensation and company performance across tech companies”
  • “Compare ESG disclosures in annual reports between 2020 and 2024”
  • “Study the impact of supply chain risks mentioned in 10-K filings on stock performance”

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For Developers

Before: Weeks of development time 
# Complex code to:
# 1. Authenticate with SEC API
# 2. Find the right filing
# 3. Parse HTML/XML content
# 4. Extract financial metrics
# 5. Handle rate limits and errors
After: Natural language integration 
User query → AI assistant → SEC EDGAR MCP → Structured response

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Design Principles

Simplicity First

Every tool should be easy to understand and use, even for non-technical users.

AI-Native

Built specifically for AI assistants with rich context and structured responses.

Comprehensive Coverage

Support all major SEC filing types and financial concepts.

Performance Focused

Fast, reliable access to data with intelligent caching and streaming.

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The Future

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Short-term Goals

  • Complete XBRL Coverage: Support all standard financial concepts
  • Advanced Analytics: Built-in financial ratio calculations and trend analysis
  • Real-time Monitoring: Alerts for new filings and material changes

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Long-term Vision

  • Global Expansion: Support for international financial data sources
  • Predictive Analytics: AI-powered insights and forecasting
  • Collaborative Features: Shared research and analysis tools

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Success Stories

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Contributing to the Vision

1

Use the Tool

The best way to contribute is to use SEC EDGAR MCP in your projects and provide feedback.
2

Share Your Story

Tell us how SEC EDGAR MCP has helped your work. Success stories inspire improvements.
3

Contribute Code

Help add new features, fix bugs, or improve documentation.
4

Spread the Word

Share SEC EDGAR MCP with others who could benefit from accessible financial data.

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Get Started

Ready to experience the future of financial data access?

Quick Start

Get up and running in 5 minutes

Use Cases

See real-world applications
Created and maintained by Stefano Amorelli. Built together with the community.