Satellite Conjunction MCP Server

An MCP (Model Context Protocol) server that tracks real satellites and checks whether two of them are on course for a close approach — the same class of problem commercial space situational awareness (SSA) providers like LeoLabs, Slingshot Aerospace, and Kayhan Space solve at scale for satellite operators.

This lets any MCP-compatible AI client (Claude Desktop, Claude Code, etc.) answer questions like:

• "Where is the ISS right now?"

• "Is Starlink-1234 going to pass close to the ISS in the next 24 hours?"

• "Screen these 10 debris objects against my satellite and tell me which ones are actually a concern."

Why this is a real problem, not a toy one

Low Earth Orbit is getting crowded. As of 2024, tracked satellites and debris routinely produce thousands of close-approach events per year that operators have to screen and respond to. Real SSA providers run this kind of screening continuously, at scale, using the same public TLE data and the same SGP4 propagation model this project uses — this server implements the same first step in that pipeline: a raw miss-distance screen used to flag which pairs of objects deserve closer attention.

What this is not: an operational collision-probability system. Real systems combine miss distance with each object's position-uncertainty (covariance) data to compute an actual probability of collision. This project reports raw miss distance and time of closest approach — the coarse first filter, not the final word. That distinction is called out directly in the tool output so it's never presented as more than it is.

Related MCP server: Satellite MCP Server

How it works

CelesTrak (live TLE data)
        │
        ▼
 tle_fetcher.py   — fetches + caches orbital elements for a satellite
        │
        ▼
 propagator.py    — SGP4 propagation (via Skyfield) → position over time
        │
        ▼
 conjunction.py   — compares two satellites' positions at matching
                     timestamps, finds the closest approach
        │
        ▼
 server.py        — exposes it all as MCP tools

• Data source: CelesTrak — free, public, no API key required. The same baseline catalog real SSA providers start from.

• Propagation model: SGP4, the standard model for TLE-based orbit prediction, via the Skyfield library.

• Distance calculation: both satellites are propagated to the same timestamps in the same reference frame, then compared directly with a Euclidean distance — this is what makes the "closest approach" number meaningful rather than an apples-to-oranges comparison.

Tools exposed

Every tool takes a NORAD catalog number — a public ID every tracked object has. A few to try:

Full catalog lookup: celestrak.org

Setup

git clone <this-repo>
cd satellite-conjunction-mcp
pip install -r requirements.txt

Run it directly

python server.py

Connect it to Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "satellite-conjunction": {
      "command": "python",
      "args": ["/absolute/path/to/satellite-conjunction-mcp/server.py"]
    }
  }
}

Restart Claude Desktop, and the four tools above become available in conversation.

Running the tests

pip install pytest
pytest tests/ -v

The test suite uses a fixed, real ISS TLE snapshot (not a live fetch) so it's deterministic and doesn't depend on network access. It checks:

• Propagated altitude falls in the expected LEO range for the ISS

• The time-stepping produces the expected number of samples

• A satellite checked against itself reports ~0 km separation at every timestep — the core sanity check that the distance math is correct

Known limitations

• TLE-based propagation has inherent uncertainty (typically growing from ~1 km to several km over days) — it is an estimate, not ground truth.

• Risk thresholds (HIGH / MODERATE / LOW) are illustrative defaults for a raw distance screen, not calibrated operational thresholds.

• This does not compute collision probability — that requires object-specific covariance data this project does not have access to.

• CelesTrak data updates roughly daily; this is not real-time tracking.

Credits

Built as a learning project to understand real satellite conjunction assessment — the kind of problem companies like LeoLabs, Slingshot Aerospace, and Digantara solve commercially.

License: MIT