🏛️ Edgar MCP Service

Model Context Protocol (MCP) Server for SEC EDGAR Database
Deep financial document analysis and content extraction service

🚀 Quick Deploy to Railway

One-Click Deployment:

1. Fork this repository to your GitHub account
2. Connect to Railway: Go to Railway → New Project → Deploy from GitHub repo
3. Set environment variable: SEC_API_USER_AGENT="Your Company/1.0 (your-email@example.com)"
4. Get your service URL from Railway dashboard
5. Done! Your MCP service is live

🎯 What This Service Provides

🔍 Universal Company Search

• Find ANY public company by name, ticker, or partial match
• Works with Apple, Netflix, small caps, recent IPOs, etc.
• No hardcoded company lists - truly universal

📄 Deep Document Analysis

• Business descriptions from 10-K Item 1
• Risk factors from 10-K Item 1A
• Financial statements with structured data
• Management discussion (MD&A) extraction
• Full-text search within any SEC filing

🔗 Advanced Filing Search

• Date range filtering: "filings between Jan-Mar 2024"
• Form type filtering: 10-K, 10-Q, 8-K, etc.
• Content search: "documents mentioning revenue recognition"
• Direct SEC EDGAR links for all results

📡 API Endpoints

Company Search

GET /search/company?q=Netflix

Response:

{
  "found": true,
  "cik": "0001065280",
  "name": "NETFLIX INC",
  "ticker": "NFLX",
  "confidence": 1.0
}

Advanced Filing Search

POST /search/filings
{
  "company": "Apple",
  "form_types": ["10-K", "10-Q"],
  "date_from": "2024-01-01",
  "content_search": "artificial intelligence",
  "limit": 10
}

Content Extraction

POST /extract/business-description
{
  "cik": "0000320193",
  "form_type": "10-K"
}

🏗️ Architecture

This MCP service is designed to work with AI query engines:

User Query → AI Engine → Edgar MCP → SEC Database
              ↓
    "Netflix's risk factors" → Company Resolution → Deep Content → Structured Response

Integration Example:

// In your AI application
const edgarMCP = 'https://your-service.up.railway.app';

// 1. Resolve company
const company = await fetch(`${edgarMCP}/search/company?q=Netflix`);

// 2. Get content
const riskFactors = await fetch(`${edgarMCP}/extract/risk-factors`, {
  method: 'POST',
  body: JSON.stringify({ cik: company.cik })
});

// 3. Use in AI analysis
const analysis = await openai.chat.completions.create({
  messages: [{ role: 'user', content: `Analyze these risk factors: ${riskFactors}` }]
});

🛠️ Manual Deployment

Prerequisites

• Python 3.11+
• Railway account
• SEC compliance: proper User-Agent string

Local Development

git clone <this-repo>
cd edgar-mcp-service
chmod +x start.sh
./start.sh

Service runs at http://localhost:8001

Deploy to Railway

railway login
railway init
railway variables set SEC_API_USER_AGENT="Your Company/1.0 (email@example.com)"
railway up

📋 Environment Variables

Variable	Required	Description	Example
SEC_API_USER_AGENT	✅	SEC API compliance identifier	"Crowe/EDGAR Query Engine 1.0 (brett.vantil@crowe.com)"
PORT	⚪	Service port (auto-set by Railway)	8001

🔒 SEC Compliance

This service is fully compliant with SEC EDGAR API requirements:

• ✅ Proper User-Agent identification
• ✅ Rate limiting respected
• ✅ Official SEC data sources only
• ✅ No data caching (always fresh)

🧪 Test Your Deployment

# Health check
curl https://your-service.up.railway.app/health

# Find any company
curl "https://your-service.up.railway.app/search/company?q=Tesla"

# Get business description
curl -X POST "https://your-service.up.railway.app/extract/business-description" \
  -H "Content-Type: application/json" \
  -d '{"cik": "0001318605", "form_type": "10-K"}'

📞 Support

This MCP service enables powerful financial analysis applications by providing:

• 🎯 Universal access to any SEC-registered company
• 📊 Deep content extraction beyond basic metadata
• 🔍 Advanced search capabilities across all filings
• 🤖 AI-ready responses for natural language processing

Perfect for building financial analysis tools, compliance monitoring, and investment research platforms.

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