🏛️ 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:

Fork this repository to your GitHub account
Connect to Railway: Go to Railway → New Project → Deploy from GitHub repo
Set environment variable: SEC_API_USER_AGENT="Your Company/1.0 (your-email@example.com)"
Get your service URL from Railway dashboard
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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