-
security-
license-
qualityA server exposing intelligent tools for enhancing RAG applications with entity extraction, query refinement, and relevance checking capabilities.
Last updated -
28
Provides deployment platform for hosting the Edgar MCP service with one-click deployment from GitHub repositories
🏛️ 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 compliance identifier |
|
| ⚪ | Service port (auto-set by Railway) |
|
🔒 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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