# Copernicus Earth Observation MCP Server
A comprehensive Model Context Protocol (MCP) server for accessing Copernicus Earth Observation data from the Copernicus Data Space ecosystem. This server provides a complete suite of tools for searching, downloading, and managing satellite imagery from all Copernicus Sentinel missions.

# 🌟 Features

## **Search & Discovery**
- **Multi-Mission Support**: Access data from Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P, and Sentinel-6 missions
- **Advanced Search**: Search by location (point, polygon, bounding box), date range, cloud cover, and mission-specific parameters
- **Coverage Analysis**: Analyze temporal coverage and availability of satellite data for specific regions
- **Recent Images**: Get the most recent satellite images for monitoring and change detection
- **Comprehensive Metadata**: Retrieve detailed image metadata including acquisition details, processing levels, and technical specifications

## **Download & Data Management**
- **Image Download**: Download full products, quicklook previews, and compressed versions of satellite images
- **Batch Operations**: Download multiple images concurrently with configurable concurrency limits
- **Intelligent Selection**: Automatic best-image selection based on recency, cloud cover, and processing level
- **Availability Checking**: Verify download availability and get file size information before downloading
- **Download Links**: Get all available download URLs for any satellite image

## **File Management**
- **File Listing**: List and analyze downloaded files with filtering by type, size, and date
- **Statistics**: Get comprehensive statistics about downloaded files (by mission, file type, time period)
- **Automated Cleanup**: Clean up old or large downloads with age-based and size-based strategies
- **Dry Run Mode**: Safety-first approach with preview of cleanup actions before execution

# 🛰️ Available Missions

| Mission | Type | Primary Applications | Resolution | Revisit Time |
|---------|------|---------------------|------------|--------------|
| **Sentinel-1** | Synthetic Aperture Radar (SAR) | Disaster monitoring, sea ice tracking, land subsidence | 5-40m | 6-12 days |
| **Sentinel-2** | Multispectral Imaging | Agriculture, vegetation monitoring, urban planning | 10-60m | 5 days |
| **Sentinel-3** | Ocean & Land Monitoring | Ocean color, sea surface temperature, fire detection | 300-1200m | <2 days |
| **Sentinel-5P** | Atmospheric Monitoring | Air quality, ozone layer, greenhouse gas tracking | 7.5×3.5km | Daily |
| **Sentinel-6** | Ocean Topography | Sea level rise, ocean circulation, climate research | 300m | 10 days |

# 🚀 Installation

## Prerequisites
- Python 3.11 or higher
- pip package manager
- Copernicus Data Space account (free registration required)

## 📦 Steps

1. **Clone the repository:**
   ```bash
   git clone <repository-url>
   cd copernicus-mcp
   ```

2. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

3. **Install in development mode:**
   ```bash
   pip install -e .
   ```

4. **Set up authentication:**
   ```bash
   # Linux/Mac
   export COPERNICUS_USERNAME="your-email@example.com"
   export COPERNICUS_PASSWORD="your-password"
   
   # Windows (Command Prompt)
   set COPERNICUS_USERNAME=your-email@example.com
   set COPERNICUS_PASSWORD=your-password
   
   # Windows (PowerShell)
   $env:COPERNICUS_USERNAME="your-email@example.com"
   $env:COPERNICUS_PASSWORD="your-password"
   ```

   or in MCP Client...
   ```json
   {
     "mcpServers": {
       "copernicus": {
         "command": "copernicus-mcp",
         "env": {
           "COPERNICUS_USERNAME": "your-email@example.com",
           "COPERNICUS_PASSWORD": "your-password"
         }
       }
     }
   }
   ```

   **Register for free at:** https://dataspace.copernicus.eu/

## Authentication Test
Verify your credentials work:
```bash
python -c "
import asyncio
import os
from copernicus_mcp.server import get_auth_token

async def test():
    result = await get_auth_token()
    if isinstance(result, dict) and 'access_token' in result:
        print('✅ Authentication successful!')
        print(f'Token length: {len(result[\"access_token\"])} characters')
    else:
        print(f'❌ Authentication failed: {result.get(\"error\", \"Unknown error\")}')

asyncio.run(test())
"
```

# 📡 Running the Server

## Basic Usage
```bash
# Run the MCP server
python -m copernicus_mcp

# Or using the module directly
python -m copernicus_mcp.server
```

## Command Line Options
```bash
# Show version
python -m copernicus_mcp --version

# Show help
python -m copernicus_mcp --help
```

# 🔧 Configuration

### Environment Variables

| Variable | Description | Required For |
|----------|-------------|--------------|
| `COPERNICUS_USERNAME` | Copernicus Data Space email | Download operations |
| `COPERNICUS_PASSWORD` | Copernicus Data Space password | Download operations |
| `COPERNICUS_DEBUG_AUTH` | Enable authentication debugging | Debugging |
| `COPERNICUS_TEST_REAL_DOWNLOAD` | Enable real download tests | Testing |

## Default Directories
- **Downloads**: `downloads/` (individual downloads)
- **Batch Downloads**: `batch_downloads/` (batch operations)
- **Search Results**: `search_downloads/` (search_and_download)

## Performance Settings
- **Max Concurrent Downloads**: 3 (configurable in `batch_download_images`)
- **API Timeout**: 60 seconds
- **Download Chunk Size**: 8KB
- **Token Cache**: 4 minutes (with 1-minute buffer)

# 🏗️ Architecture

## Server Structure
```
copernicus-mcp/
├── copernicus_mcp/          # Main package
│   ├── server.py           # Complete server implementation
│   ├── __init__.py         # Package exports
│   └── server_corrupted_backup.py  # Backup
├── examples/               # Usage examples
│   └── example_download_usage.py
├── requirements.txt        # Python dependencies
├── pyproject.toml         # Project configuration
├── README.md              # This file
├── mcp_config.json        # MCP client configuration
└── INSTALL.md             # Installation guide
```

## Key Components
1. **Authentication Manager**: Handles token acquisition, caching, and refresh
2. **Search Engine**: Advanced query builder for Copernicus Data Space API
3. **Download Manager**: Concurrent downloads with progress tracking
4. **File Manager**: Disk space management and cleanup
5. **MCP Interface**: FastMCP-based tool registration and protocol handling


# 🛠️ Available Tools

## **Search & Discovery Tools**

### `search_copernicus_images`
Search for satellite images from Copernicus missions.

**Parameters:**
- `geometry`: GeoJSON polygon coordinates, point [lon, lat], or bbox [min_lon, min_lat, max_lon, max_lat]
- `geometry_type`: 'point', 'polygon', or 'bbox'
- `mission`: Mission name ('sentinel-1', 'sentinel-2', 'sentinel-3', 'sentinel-5p', 'sentinel-6')
- `start_date`, `end_date`: Date range (YYYY-MM-DD)
- `max_cloud_cover`: Maximum cloud cover percentage (0-100, optical missions only)
- `max_results`: Maximum number of results (1-1000)

**Example:**
```python
# Search for Sentinel-2 images over Paris
search_copernicus_images(
    geometry=[[2.2945, 48.8584], [2.2945, 48.8604], [2.2965, 48.8604], [2.2965, 48.8584]],
    geometry_type="polygon",
    mission="sentinel-2",
    start_date="2024-01-01",
    end_date="2024-01-31",
    max_cloud_cover=20,
    max_results=10
)
```

### `get_image_details`
Get comprehensive metadata for a specific satellite image.

**Parameters:**
- `image_id`: Satellite image ID (from search results)
- `mission`: Optional mission name

**Returns:** Detailed metadata including download URLs, processing level, cloud cover, footprint, and authentication guidance.

### `get_mission_info`
Get detailed information about Copernicus satellite missions.

**Parameters:**
- `mission`: Optional specific mission name

**Returns:** Mission capabilities, sensors, applications, resolution, and revisit time.

### `get_recent_images`
Get the most recent satellite images for a region.

**Parameters:**
- `geometry`: Location coordinates
- `geometry_type`: 'point', 'polygon', or 'bbox'
- `mission`: Mission name
- `days_back`: Number of days to look back (default: 7)
- `max_results`: Maximum results (default: 10)

### `check_coverage`
Analyze satellite image coverage for a region over time.

**Parameters:**
- `geometry`: Location coordinates
- `geometry_type`: 'point', 'polygon', or 'bbox'
- `mission`: Mission name
- `start_date`, `end_date`: Analysis period
- `group_by`: Group results by 'day', 'week', 'month', or 'year'

## **Download Tools**

### `download_image`
Download a Copernicus satellite image by ID.

**Parameters:**
- `image_id`: Image ID from search results (required)
- `mission`: Mission name (default: 'sentinel-2')
- `download_type`: 'full', 'quicklook', or 'compressed' (default: 'full')
- `output_dir`: Custom output directory (default: 'downloads')

**Example:**
```python
# Download a quicklook preview
download_image(
    image_id="S2B_MSIL2A_20240115T105629_N0510_R094_T31UCS_20240115T130259",
    mission="sentinel-2",
    download_type="quicklook"
)

# Download full product
download_image(
    image_id="S2B_MSIL2A_20240115T105629_N0510_R094_T31UCS_20240115T130259",
    mission="sentinel-2",
    download_type="full"
)
```

### `batch_download_images`
Download multiple images concurrently.

**Parameters:**
- `image_ids`: List of image IDs to download
- `mission`: Mission name (default: 'sentinel-2')
- `download_type`: 'full', 'quicklook', or 'compressed' (default: 'full')
- `output_dir`: Output directory (default: 'batch_downloads')
- `max_concurrent`: Maximum concurrent downloads (default: 3)

**Example:**
```python
batch_download_images(
    image_ids=["id1", "id2", "id3"],
    mission="sentinel-2",
    download_type="quicklook",
    max_concurrent=2
)
```

### `search_and_download`
Search for images and automatically download the best match.

**Parameters:**
- `geometry`: Location coordinates
- `geometry_type`: 'point', 'polygon', or 'bbox' (default: 'point')
- `mission`: Mission name (default: 'sentinel-2')
- `start_date`, `end_date`: Search date range
- `max_cloud_cover`: Maximum cloud cover percentage
- `download_type`: 'full', 'quicklook', or 'compressed' (default: 'quicklook')
- `output_dir`: Output directory
- `limit`: Maximum search results to consider (default: 5)

**Example:**
```python
# Search and download best image
search_and_download(
    geometry=[-122.4194, 37.7749],  # San Francisco
    geometry_type="point",
    mission="sentinel-2",
    start_date="2024-01-01",
    end_date="2024-01-31",
    download_type="quicklook"
)
```

### `check_download_availability`
Check if images are available for download.

**Parameters:**
- `image_ids`: List of image IDs to check

**Returns:** Availability status, file sizes, and quicklook availability for each image.

### `get_product_download_links`
Get all available download links for an image.

**Parameters:**
- `image_id`: Image ID

**Returns:** All download URLs (full, compressed, quicklooks) with metadata.

## **File Management Tools**

### `list_downloaded_files`
List downloaded satellite image files.

**Parameters:**
- `download_dir`: Directory to scan (default: 'downloads')
- `file_type`: Filter by 'full', 'quicklook', 'compressed', or None for all
- `limit`: Maximum files to return (default: 50)

**Example:**
```python
list_downloaded_files(
    download_dir="my_downloads",
    file_type="quicklook",
    limit=10
)
```

### `cleanup_downloads`
Clean up downloaded files based on criteria.

**Parameters:**
- `download_dir`: Directory to clean (default: 'downloads')
- `older_than_days`: Remove files older than X days
- `max_size_mb`: Keep total size under X MB (removes oldest first)
- `file_type`: Filter by file type
- `dry_run`: Only show what would be deleted (default: True)

**Example:**
```python
# Dry run - see what would be deleted
cleanup_downloads(
    download_dir="downloads",
    older_than_days=30,
    dry_run=True
)

# Actually delete files older than 30 days
cleanup_downloads(
    download_dir="downloads",
    older_than_days=30,
    dry_run=False
)

# Keep total size under 10GB
cleanup_downloads(
    download_dir="downloads",
    max_size_mb=10240,
    dry_run=False
)
```

### `get_download_statistics`
Get statistics about downloaded files.

**Parameters:**
- `download_dir`: Directory to analyze (default: 'downloads')

**Returns:** Comprehensive statistics including total files, size, breakdown by mission/file type/month, and oldest/newest files.

# 📊 Complete Workflow Example

```python
# 1. Search for images
search_results = search_copernicus_images(
    geometry=[-122.4194, 37.7749],  # San Francisco
    geometry_type="point",
    mission="sentinel-2",
    start_date="2024-01-01",
    end_date="2024-01-31",
    max_cloud_cover=30,
    max_results=5
)

# 2. Extract image IDs
image_ids = [img["Id"] for img in search_results.get("products", [])]

# 3. Check availability
availability = check_download_availability(image_ids[:2])

# 4. Download quicklooks for available images
for image_id in image_ids[:2]:
    download_image(
        image_id=image_id,
        mission="sentinel-2",
        download_type="quicklook"
    )

# 5. List downloaded files
files = list_downloaded_files(
    download_dir="downloads",
    file_type="quicklook"
)

# 6. Get statistics
stats = get_download_statistics()
```

# 🔒 Authentication Model

## Public Access (No Authentication Required)
- Mission information
- Basic search operations
- Metadata retrieval

## Authenticated Access (Credentials Required)
- Image downloads (full, quicklook, compressed)
- Batch downloads
- Availability checks
- Download link retrieval

## Token Management
- Automatic token acquisition from Copernicus Identity Service
- Token caching with expiration handling
- Graceful error handling for invalid credentials
- Support for both environment variables and parameter-based authentication

# ⚠️ Error Handling

The server includes comprehensive error handling for:

## Authentication Errors
- Missing credentials
- Invalid credentials
- Token expiration
- Rate limiting

## API Errors
- Invalid image IDs
- Unavailable products
- Network timeouts
- API quota exceeded

## File System Errors
- Insufficient disk space
- Permission denied
- Invalid file paths
- Corrupted downloads

## User Input Errors
- Invalid geometry formats
- Unsupported mission parameters
- Date range errors
- Invalid download types

# ⚡ Performance Considerations

## **Download Performance**
- **Progress Reporting**: Updates every 10 seconds or 100MB
- **Extended Timeouts**: Up to 2 hours for large downloads
- **Chunk Size**: 1MB chunks for optimal throughput
- **Concurrent Downloads**: Configurable (default: 3 concurrent)

## **Timeout Configuration**
The server uses appropriate timeouts for different operations:
- **Small files (quicklooks)**: 2 minutes
- **Medium files (compressed)**: 1 hour  
- **Large files (full products)**: 2 hours
- **API requests**: 1 minute

## **MCP Client Compatibility**
- All progress sent to `stderr` with regular flushing
- Clients must monitor `stderr` for progress updates
- Connection kept alive through periodic output

## Download Sizes
- **Quicklooks**: 100KB - 1MB (recommended for testing)
- **Compressed Products**: 100MB - 1GB
- **Full Products**: 1GB - 10GB+ (varies by mission)

## Network Usage
- Start with quicklook downloads for testing
- Use `max_concurrent` to control bandwidth usage
- Monitor disk space for large downloads

# 🚨 Security Notes

## Credential Safety
- Never hardcode credentials in code
- Use environment variables or secure credential stores
- Tokens are automatically refreshed and never stored permanently
- All authentication errors are logged without exposing sensitive information

## Network Security
- All API calls use HTTPS with proper certificate validation
- Timeout settings prevent hanging connections

## File Security
- Downloaded files use standard file permissions
- No automatic execution of downloaded content
- Cleanup operations require explicit confirmation (dry-run mode by default)

# 🔧 Troubleshooting

## **Testing Your Setup**
```bash
# Test timeout fixes
python test_download_timeout.py

# Test with example downloads
python example_download_usage.py
```

## Common Issues and Solutions

### Authentication Failures
```bash
# Check if credentials are set
echo $COPERNICUS_USERNAME
echo $COPERNICUS_PASSWORD

# Test authentication directly
python -c "
import asyncio
from copernicus_mcp.server import get_auth_token
async def test():
    result = await get_auth_token()
    print('Result:', result)
asyncio.run(test())
"
```

### Download Failures
1. **Check disk space**: Ensure you have sufficient space for downloads
2. **Verify image ID**: Use valid IDs from search results
3. **Try quicklook first**: Test with smaller files before downloading full products
4. **Check network**: Ensure stable internet connection

### Search Issues
1. **Date range**: Use reasonable date ranges (e.g., last 30 days)
2. **Geometry size**: Keep search areas manageable
3. **Cloud cover**: Adjust cloud cover filters for optical missions

## Debug Mode
Enable debug logging for detailed information:
```bash
export COPERNICUS_DEBUG_AUTH=true
python -m copernicus_mcp
```

## Log Files
- Check application logs for detailed error messages
- Monitor download progress in real-time
- Review cleanup operations before execution

# 🚨 Zed Timeout Fixes

## Issue Description
Zed IDE (and other MCP clients) expect MCP tools to complete quickly (typically within 30-60 seconds). However, full satellite image downloads might take **hours** to complete. This mismatch causes Zed to kill download processes prematurely.
you can increase MCP Tool timeout in Zed settings.

# 🙏 Acknowledgments

## Data Providers
- **European Space Agency (ESA)** for the Copernicus program
- **Copernicus Data Space Ecosystem** for providing API access
- **European Commission** for funding and support

## Technical Dependencies
- **FastMCP** framework for MCP server implementation
- **httpx** for async HTTP client functionality
- **pydantic** for data validation and serialization
- **shapely** for geometric operations

# 📚 Additional Resources

## Documentation
- [Copernicus Data Space Documentation](https://documentation.dataspace.copernicus.eu/)
- [MCP Protocol Specification](https://spec.modelcontextprotocol.io/)
- [FastMCP Documentation](https://fastmcp.readthedocs.io/)

## Tutorials and Examples
- Complete workflow examples in `example_download_usage.py`
- Configuration examples in `mcp_config.json`