Astrodynamics MCP Server

astrodynamics-mcp

A Model Context Protocol server that gives any MCP-capable LLM client (Claude Code, Cursor, ChatGPT desktop, custom agents) authoritative astrodynamics tools: TLE/SGP4 propagation, Lambert solving, ground-station access, time-scale and coordinate-frame conversions, porkchop scans, B-plane targeting.

Why

LLMs reason well about astrodynamics concepts but cannot do the numerical work — they cannot propagate orbits, solve Lambert problems, or query SPICE ephemerides. astrodynamics-mcp lets you plug authoritative tools into any MCP-capable client so the LLM calls vetted upstream libraries instead of fabricating numbers. Every result carries explicit units; every tool description tunes against an Inspect AI eval suite that measures whether the LLM picks the right tool and binds the right arguments.

Tools

Full input / output JSON schemas live on the Tool reference page of the docs site.

Quick start

Install:

uv tool install astrodynamics-mcp     # or: pipx install astrodynamics-mcp

Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "astrodynamics": {
      "command": "astrodynamics-mcp",
      "args": ["stdio"]
    }
  }
}

Restart Claude Code. In a chat:

You: Compute the Hohmann Δv from a 250 km circular LEO to GEO.

(The model calls lambert_solve with the Hohmann geometry and answers ≈ 3.91 km/s, citing the tool output — not the LLM's own weights.)

Cursor

~/.cursor/mcp.json (or workspace-level .cursor/mcp.json):

{
  "mcpServers": {
    "astrodynamics": {
      "command": "astrodynamics-mcp",
      "args": ["stdio"]
    }
  }
}

Restart Cursor. The tools appear under the astrodynamics server group.

See Pick a client in the docs for ChatGPT desktop, a raw Python MCP smoke client, and the Streamable HTTP transport for remote agents.

Supported clients

What this is not

• Not a general-purpose astrodynamics framework. Wraps vetted upstream libraries; does not re-implement propagators, integrators, or coordinate systems.
• Not an agent framework. Exposes MCP tools; LangGraph, AutoGen, CrewAI, and the LLM clients themselves consume them.
• Not an ML / inference server. Tools that need their own ML models (maneuver detection, neural propagators) belong in separate MCP servers — kept modular for dependency isolation.
• Not a SaaS. Runs locally or in your own infrastructure. No hosted multi-tenant deployment.
• Not a web UI. Tool consumption is via MCP clients; no browser frontend, no desktop app, no notebook widget.

For direct (non-MCP) Python use of the same surfaces, reach for the upstream libraries: sgp4, lamberthub, skyfield, astropy, interplanetary-porkchop, spiceypy.

Built on

The official Anthropic modelcontextprotocol/python-sdk (MIT). The bundled FastMCP server class is the server primitive; stdio + Streamable HTTP transports are first-class.

Docs and links

• Docs site: astro-tools.github.io/astrodynamics-mcp — getting started, per-client setup, tool reference, recipes, data sources, eval suite, FAQ.
• Issue tracker: astro-tools/astrodynamics-mcp/issues
• Discussions: orgs/astro-tools/discussions — usage help and open-ended questions.
• Eval suite: eval/README.md — the regression contract on tool-description quality.

License

MIT — see LICENSE.