Satellite MCP Server

A comprehensive Model Context Protocol (MCP) server for satellite orbital mechanics calculations with natural language processing capabilities.

✨ Key Features

• 🛰️ Satellite Access Window Calculations - Calculate when satellites are visible from ground locations

• 🌍 World Cities Database - Built-in database of 200+ cities worldwide for easy location lookup

• 🗣️ Natural Language Processing - Parse orbital parameters from text like "satellite at 700km in SSO over London"

• 📡 TLE Generation - Generate Two-Line Elements from orbital descriptions

• 🌅 Lighting Analysis - Ground and satellite lighting conditions (civil, nautical, astronomical twilight)

• 📊 Bulk Processing - Process multiple satellites and locations from CSV data

• 🚀 6 Orbit Types - Support for LEO, MEO, GEO, SSO, Molniya, and Polar orbits

Related MCP server: N2YO Satellite Tracker MCP Server

🚀 Quick Start

Using Docker (Recommended)

# Clone the repository
git clone <repository-url>
cd mcp-orbit

# Build the Docker image
make docker-build

# Run the MCP server
make docker-run

Local Installation

# Install dependencies
make install

# Run the MCP server
make run

🔌 Connecting to the MCP Server

The server communicates via JSON-RPC 2.0 over stdio. Here are the connection methods:

Claude Desktop Integration

Add to your Claude Desktop MCP configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "satellite-mcp-server": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "satellite-mcp-server:latest"]
    }
  }
}

Direct Docker Connection

# Interactive mode
docker run -it --rm satellite-mcp-server:latest

# Pipe commands
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | \
  docker run --rm -i satellite-mcp-server:latest

Local Python Connection

# If running locally without Docker
python -m src.mcp_server

💬 Example Usage in LLMs

Example 1: Basic Satellite Pass Prediction

User Prompt:

"When will the ISS be visible from London tomorrow?"

MCP Tool Call:

{
  "tool": "calculate_access_windows_by_city",
  "arguments": {
    "city_name": "London",
    "tle_line1": "1 25544U 98067A   24001.50000000  .00001234  00000-0  12345-4 0  9999",
    "tle_line2": "2 25544  51.6400 123.4567 0001234  12.3456 347.6543 15.49011999123456",
    "start_time": "2024-01-02T00:00:00Z",
    "end_time": "2024-01-03T00:00:00Z"
  }
}

Response: The ISS will be visible from London 4 times tomorrow, with the best pass at 19:45 UTC reaching 78° elevation in the southwest sky during civil twilight.

Example 2: Natural Language Orbital Design

User Prompt:

"Create a sun-synchronous satellite at 700km altitude and show me when it passes over Tokyo."

MCP Tool Calls:

1. Parse orbital elements:

{
  "tool": "parse_orbital_elements",
  "arguments": {
    "orbital_text": "sun-synchronous satellite at 700km altitude"
  }
}

2. Calculate access windows:

{
  "tool": "calculate_access_windows_from_orbital_elements_by_city",
  "arguments": {
    "orbital_text": "sun-synchronous satellite at 700km altitude",
    "city_name": "Tokyo",
    "start_time": "2024-01-01T00:00:00Z",
    "end_time": "2024-01-02T00:00:00Z"
  }
}

Response: Generated SSO satellite (98.16° inclination, 98.6 min period) with 14 passes over Tokyo in 24 hours, including 6 daylight passes and 8 during various twilight conditions.

Example 3: Bulk Satellite Analysis

User Prompt:

"I have a CSV file with ground stations and want to analyze coverage for multiple satellites."

MCP Tool Call:

{
  "tool": "calculate_bulk_access_windows",
  "arguments": {
    "locations_csv": "name,latitude,longitude,altitude\nMIT,42.3601,-71.0589,43\nCaltechm,34.1377,-118.1253,237",
    "satellites_csv": "name,tle_line1,tle_line2\nISS,1 25544U...,2 25544...\nHubble,1 20580U...,2 20580...",
    "start_time": "2024-01-01T00:00:00Z",
    "end_time": "2024-01-02T00:00:00Z"
  }
}

🛠️ Available Tools

1. calculate_access_windows - Basic satellite visibility calculations

2. calculate_access_windows_by_city - City-based satellite passes

3. calculate_bulk_access_windows - Multi-satellite/location analysis

4. parse_orbital_elements - Natural language orbital parameter parsing

5. calculate_access_windows_from_orbital_elements - Access windows from orbital text

6. calculate_access_windows_from_orbital_elements_by_city - Combined orbital elements + city lookup

7. search_cities - Find cities in the world database

8. validate_tle - Validate Two-Line Element data

9. get_orbit_types - Available orbit type definitions

🗂️ Project Structure

/
├── src/
│   ├── mcp_server.py          # MCP server implementation
│   ├── satellite_calc.py      # Core orbital mechanics calculations
│   └── world_cities.py        # World cities database
├── docs/                      # Documentation
├── Dockerfile                 # Container definition
├── docker-compose.yml         # Multi-container setup
└── Makefile                   # Build automation

📚 Dependencies

• Skyfield - Satellite position calculations

• NumPy - Numerical computations

• MCP - Model Context Protocol implementation

• Python 3.8+ - Runtime environment

🤝 Contributing

This is a specialized MCP server for satellite orbital mechanics. For issues or enhancements, please check the documentation in the docs/ directory.

📄 License

[Add your license information here]