What if your AI assistant could read your emails, check your calendar, update spreadsheets, and analyze business data - all automatically? That's what AI agents can do, but connecting them to your data used to be complicated.
Now there's a simple solution that makes AI work with all your tools and data.
Think of an AI Agent like a smart assistant that can actually DO things, not just answer questions
Read emails, write responses, organize by priority, schedule meetings
Pull data from spreadsheets, databases, apps and create reports
Check availability, book meetings, send reminders, coordinate with others
Order supplies, book travel, make purchases based on your preferences
To do all this, the AI agent needs to connect to your email, calendar, spreadsheets, databases, and other apps. Before MCP, this was incredibly difficult and expensive to set up.
Imagine having a brilliant assistant who's locked in a room with no phone, computer, or way to access your information
AI can think but can't access your actual data or tools
AI can access and work with all your tools and data
Real examples of how AI agents save time and get work done automatically
This seems impossible, but it's actually simple. The AI agent needs three things:
The AI needs to read your email, access your spreadsheets, connect to your databases, etc.
Not just read data, but actually do things like send emails, update files, create reports
Know what tools to use when, and how to combine information from different sources
Connecting AI to all your different tools and data sources used to be incredibly difficult and expensive. Each connection was custom-built and could take months to develop.
The simple technology that makes AI agents possible
Imagine if every device needed a different cable for every other device. That's how AI connections worked before MCP. Want your AI to read emails AND check databases? You needed separate custom code for each. MCP fixes this by creating one standard way for AI to connect to everything.
Custom connector for each combination
Single standard for all connections
Like how USB-C works with any device, MCP works with any data source. Connect AI to databases, files, websites, or apps using the same simple method.
Tools: Actions AI can take (send email, search web)
Resources: Information AI can read (files, databases)
Prompts: Ready-made instructions for common tasks
AI gets instant access to live data while keeping your information secure. You control exactly what the AI can see and do.
Claude Desktop, VS Code, or custom apps initiate connections
Manages protocol communication, handles requests/responses
Exposes tools, resources, and prompts to AI models
Before MCP, connecting AI to data was complicated and expensive. Now it's simple and fast.
Every AI app needed custom code for every data source. Want to connect 10 apps to 20 databases? That's 200 different integrations to build and maintain.
Every connection used different security methods, making it hard to keep data safe and meet compliance requirements.
When one service updated their system, it could break multiple AI apps. Every change meant updating lots of custom code.
Create one MCP connection and any AI app can use it. Build one AI app and it can connect to any MCP-enabled service.
Built-in authentication, permissions, and audit logging. Security best practices enforced by the protocol itself.
Protocol versioning and capability negotiation ensure backward compatibility. New features don't break existing integrations.
Custom API wrappers for each integration
Anthropic introduces MCP standard
Universal AI connectivity standard
Real example of an AI agent that saves investors and business owners hours every week
🤔 The Problem: Reading company earnings reports is boring, time-consuming, and most people don't know what to look for. But you need this information to make smart business decisions.
EarningsAgent is like having a super-fast financial analyst that never gets tired. It can read earnings reports from dozens of companies at the same time and pull out the important numbers in minutes, not hours.
While other tools just display raw data, EarningsAgent actually reads and understands earnings reports like a human analyst - but much faster and without getting tired.
Automatically pulls out important numbers like revenue, profits, and company forecasts from earnings reports and financial news
Can research multiple companies at the same time, turning hours of work into just a few minutes
Generates clean summaries and data files that you can easily read or import into other tools like Excel
Built to handle errors gracefully and keep working even when things go wrong, with detailed logs of what happened
Company ticker symbols (AAPL, NVDA, GOOGL)
Tavily API searches financial sources
Extracts structured financial metrics
CSV/JSON reports with insights
Here's what EarningsAgent produces when analyzing Nike's latest earnings - all generated automatically in under 2 minutes using real data from our system
Nike faced a challenging quarter with $11.1 billion in revenue, down 12% year-over-year as the athletic apparel giant navigates market headwinds. Despite beating estimates by 3.4%, Nike's declining margins (down 440 basis points to 40.3%) and cautious FY2026 guidance of $45.21 billion reflect ongoing challenges in demand and increased competition in the athletic wear market.
| Metric | Current Period | Prior Year | Growth | Status |
|---|---|---|---|---|
| Total Sales (This Quarter) | $11.1B | $12.61B | -12% | Confirmed |
| Total Sales (Full Year Guidance) | $45.21B | $49.32B | -10% | Updated |
| Earnings Per Share | $0.14 | $1.01 | -86% | Confirmed |
| Gross Margin | 40.3% | 44.7% | -440 bps | Confirmed |
| Next Quarter Prediction | $10.74B | $10.38B (Est.) | Beat by 3.4% | Forecast |
{
"company": "Nike Inc.",
"ticker": "NKE",
"analysis_date": "2025-06-28T21:50:54Z",
"processing_time_seconds": 112,
"data_sources": [
"Official SEC Filing",
"Earnings Call Recording",
"Financial News Articles"
],
"financial_metrics": {
"revenue": {
"current_quarter": {
"value": 11100000000,
"period": "Q4 2025",
"currency": "USD",
"growth_yoy": -12,
"confidence": "high"
},
"annual_estimate": {
"value": 45210000000,
"period": "FY 2026 (guidance)",
"growth_yoy": -10,
"confidence": "high"
}
},
"guidance": {
"next_quarter_prediction": {
"value": 10740000000,
"period": "Q1 2026",
"range": "$10.6B to $10.9B",
"vs_estimates": "above by 3.4%",
"confidence": "official_company_forecast"
},
"next_fiscal_year": {
"value": 45210000000,
"period": "FY 2026",
"currency": "USD"
}
},
"profitability": {
"gross_profit": {
"value": 4477300000,
"margin_percent": 40.3,
"growth_yoy": -440,
"period": "Q4 2025"
}
}
},
"segment_performance": {
"nike_direct": {
"revenue": 5000000000,
"growth_yoy": -8,
"percentage_of_total": 45.0
},
"wholesale": {
"revenue": 5600000000,
"growth_yoy": -15,
"percentage_of_total": 50.5
},
"other": {
"revenue": 500000000,
"growth_yoy": -10,
"percentage_of_total": 4.5
}
},
"creative_insights": {
"key_themes": [
"Inventory management challenges",
"Direct-to-consumer strategy shifts",
"Competition from newer athletic brands",
"Global economic headwinds"
],
"risk_factors": [
"Increased tariffs and trade costs",
"Softening consumer demand",
"Wholesale partner challenges",
"Currency exchange fluctuations"
],
"growth_drivers": [
"Digital commerce expansion",
"Innovation in product technology",
"Growing women's and Jordan brand",
"Emerging market opportunities"
]
},
"data_quality": {
"completeness_score": 94,
"confidence_level": "high",
"source_reliability": "primary_sources",
"last_verified": "2025-06-28T21:50:54Z"
}
}Analyze 500+ companies daily to identify investment opportunities based on earnings momentum, guidance revisions, and margin trends
Track guidance patterns and management commentary to predict earnings surprises before they happen
Monitor entire sectors (semiconductor, software, biotech) to identify emerging trends and shifts in competitive dynamics
Track revenue and guidance patterns across supply chains to identify bottlenecks and opportunities
Rapidly analyze acquisition targets and their competitors to build comprehensive financial profiles
Monitor borrower financial health through continuous earnings and guidance tracking
EarningsAgent pioneers the use of Large Language Models for structured financial data extraction. Unlike rule-based systems, it understands context, nuance, and financial language like a human analyst.
Traditional financial research is sequential - one company at a time. EarningsAgent processes multiple companies simultaneously, reducing research time from days to minutes.
Moves financial research from static, periodic analysis to continuous, real-time intelligence gathering that adapts to market changes instantly.
Levels the playing field by giving smaller firms access to the same quality of financial intelligence that was previously available only to large institutions.
Explore how MCP and EarningsAgent are transforming their respective domains through standardization, automation, and AI-powered intelligence