# Axion MCP Server Integration
## Foundation Model vs. Product Delivery
**Understanding the distinction is critical:**
### 🧠 Axion Foundation Model
The neural network brain. Trained on massive datasets, achieves 86.66% mIoU accuracy, beats all existing architectures. This is our research contribution.
### 🔧 AWS Raw Inference
Direct model access for researchers and developers who want to explore the neural network. You get raw predictions, nothing more. Test the model, run experiments, see outputs.
### 🚀 MCP Server (Complete Platform)
**This is where the magic happens.** The MCP server provides the full end-to-end platform:
- SAR data acquisition and preprocessing
- Model inference with our exclusive Axion model
- Multi-modal output processing
- Analysis and insights generation
- AI agent natural language interface
- Production deployment tools
## Flow Diagram
```
┌─────────────────────┐
│ SAR Radar Data │
└──────────┬──────────┘
│
▼
┌─────────────────────────────────┐
│ Axion Foundation Model │
│ (TerraMind Encoder + DARN) │
└──────────┬──────────────────────┘
│
├──────────────────────┐
│ │
▼ ▼
┌──────────────────┐ ┌──────────────────────┐
│ AWS RAW │ │ MCP SERVER │
│ INFERENCE │ │ (Full Platform) │
├──────────────────┤ ├──────────────────────┤
│ Neural Network │ │ Complete Solution │
│ Output │ │ │
│ • Raw predictions│ │ • Data processing │
│ • No pipeline │ │ • Model inference │
│ • Research only │ │ • Analysis tools │
└──────────────────┘ │ • AI agent interface │
│ • Production ready │
└──────────────────────┘
```
## Exclusive MCP Features
The following features are **ONLY** available through the hosted MCP server:
### 1. Automated Data Acquisition
```typescript
// MCP handles data fetching automatically
const result = await axion.analyze({
location: [37.7749, -122.4194],
timeRange: '2025-01-01/2025-01-31'
});
// SAR data, DEM, coordinates all handled for you
```
### 2. Multi-Modal Processing Pipeline
```typescript
// Get all modalities processed and analyzed
const processed = await axion.processOutputs({
rgb: true,
vegetation: true,
landCover: true,
elevation: true
});
```
### 3. Natural Language Interface
```typescript
// AI agents can query in natural language
const insights = await axion.query(
"Show me crop health changes in California's Central Valley over the past month"
);
```
### 4. Real-Time Analysis
```typescript
// Automated change detection and alerts
await axion.monitor({
region: californiaFarms,
alerts: ['vegetation_stress', 'flood_risk'],
frequency: 'daily'
});
```
### 5. Production Deployment
- Scalable infrastructure
- API rate limiting
- Authentication and access control
- Data caching and optimization
- Monitoring and logging
## Why Use MCP vs. Raw Inference?
| Feature | AWS Raw Inference | MCP Server |
|---------|------------------|------------|
| Model Access | ✅ Direct | ✅ Optimized |
| Data Processing | ❌ Manual | ✅ Automated |
| Multi-Modal Output | ❌ Raw only | ✅ Processed |
| AI Agent Integration | ❌ No | ✅ Native |
| Analysis Tools | ❌ None | ✅ Complete |
| Production Ready | ❌ No | ✅ Yes |
| Natural Language | ❌ No | ✅ Yes |
| Real-Time Monitoring | ❌ No | ✅ Yes |
## Getting Started with MCP
### Installation
```bash
npm install @axion-orbital/mcp-server
```
### Configuration
```typescript
import { AxionMCP } from '@axion-orbital/mcp-server';
const axion = new AxionMCP({
endpoint: 'https://mcp.axionorbital.space',
// No API key required - open source!
});
```
### Basic Usage
```typescript
// Analyze a specific location
const analysis = await axion.analyzeSAR({
coordinates: {
lat: 37.7749,
lon: -122.4194
},
date: '2025-01-15',
modalities: ['rgb', 'ndvi', 'lulc']
});
console.log(analysis.rgb); // Optical-quality image
console.log(analysis.ndvi); // Vegetation index
console.log(analysis.lulc); // Land cover classification
```
### AI Agent Integration
```typescript
// Works with Claude, ChatGPT, or any MCP-compatible system
import { MCPClient } from '@modelcontextprotocol/sdk';
const client = new MCPClient();
await client.connect('axion-orbital-mcp');
// Now your AI agent can access Axion's capabilities
const response = await client.query(
"Analyze agricultural changes in Iowa from January to March 2025"
);
```
## Architecture
### MCP Server Components
1. **Data Ingestion Layer**
- Sentinel-1 SAR data acquisition
- DEM and coordinate processing
- Metadata extraction
2. **Axion Model Inference**
- Exclusive access to full Axion foundation model
- Optimized batch processing
- GPU acceleration
3. **Post-Processing Pipeline**
- Multi-modal output generation
- Image enhancement
- Statistical analysis
4. **API Layer**
- REST and WebSocket endpoints
- Model Context Protocol (MCP) interface
- Natural language query processing
5. **Analysis Engine**
- Change detection algorithms
- Anomaly identification
- Trend analysis
## Performance
- **Latency:** ~2-5s for complete analysis (vs. ~10-15s for raw inference + manual processing)
- **Throughput:** 1000+ requests/minute
- **Availability:** 99.9% uptime SLA
- **Coverage:** Global SAR data access
## Pricing
The MCP server is **open-source** with no API keys required for basic usage. Enterprise features available for production deployments.
## Support
- **Documentation:** https://docs.axionorbital.space
- **GitHub:** https://github.com/axion-orbital/mcp-server
- **Discord:** https://discord.gg/axion-orbital
---
**Remember:** While raw inference is available for research, the MCP server provides the complete, production-ready platform with exclusive features for real-world applications.
