Satellite MCP Server

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

Clone the repository

git clone cd mcp-orbit

Build the Docker image

make docker-build

Run the MCP server

make docker-run

Local Installation

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

🛠️ Available Tools

• calculate_access_windows - Basic satellite visibility calculations

• calculate_access_windows_by_city - City-based satellite passes

• calculate_bulk_access_windows - Multi-satellite/location analysis

• parse_orbital_elements - Natural language orbital parameter parsing

• calculate_access_windows_from_orbital_elements - Access windows from orbital text

• calculate_access_windows_from_orbital_elements_by_city - Combined orbital elements + city lookup

• search_cities - Find cities in the world database

• validate_tle - Validate Two-Line Element data

• 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]

🚀 Quick Start

Using Docker (Recommended)

Install dependencies

make install

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:

• Parse orbital elements:

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

• 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."

{ "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" } }