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, satellite metadata, and — with optional extras — full NASA GMAT mission execution ([gmat]), NASA SPICE / NAIF kernel queries ([spice]), and trajectory visualisation ([viz]).

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

| Tool | What it does | Backed by | | ----------------- | --------------------------------------------------------- | ------------------------ | | tle_lookup | Fetch current TLEs by NORAD ID, name, or group — from CelesTrak (default) or Space-Track. | CelesTrak gp.php API · Space-Track † | | sgp4_propagate | Propagate TLEs across UTC ISO 8601 epochs in TEME / ICRF / GCRS / ITRS / CIRS. | sgp4 | | lambert_solve | Solve Lambert's problem; multi-rev solutions enumerated; two-impulse Δv on demand. | lamberthub | | access_windows | Ground-station / observer access intervals over a window, with AOS / LOS / peak elevation. | skyfield | | time_convert | UTC / TAI / TT / TDB / UT1 / GPS / TCB / TCG conversions across ISO / JD / MJD / J2000-seconds / Unix. | astropy.time | | frame_transform | State-vector transforms across ICRF / ITRS / GCRS / TEME / CIRS / TIRS / IAU body-fixed frames. | astropy.coordinates | | porkchop | (depart × arrive) Δv / C3 grid for interplanetary transfers, ASCII contour, summary or full output. | lamberthub + JPL Horizons | | bplane_target | B-plane element calculation and impulsive targeting for hyperbolic flybys. | in-house, JPL Horizons fed | | satellite_metadata | Physical & provenance metadata (mass, dimensions, COSPAR ID, launch, operator, decay status) for a NORAD ID. | ESA DISCOSweb † |

† Credentialed source. Pass credentials as environment variables for the stdio transport, or in the session-init _meta block for HTTP — see Credentials. A tool called without its credential returns a typed CredentialRequiredError, never a silent failure.

GMAT tools (optional `[gmat]` extra)

Install the [gmat] extra and have a local NASA GMAT install, and five more tools register for driving real GMAT missions (they stay hidden otherwise):

| Tool | What it does | Backed by | | ----------------------- | --------------------------------------------------------- | -------------- | | gmat_run_mission | Run a complete GMAT mission; returns a parsed summary, report data, and pointers to large outputs. | gmat-run | | gmat_sweep | Parameter sweeps and Monte Carlo (grid / samples / Monte Carlo / Latin hypercube) over a mission. | gmat-sweep | | gmat_execute_script | Escape hatch — run raw GMAT script text and return its reports verbatim; engine errors come back as data. | gmat-run | | gmat_validate_script | Parse-validate a script without running it; returns errors, warnings, and the resource/command structure. | gmat-run | | gmat_read_run_artefact| Read the raw text of a file produced by a prior run (ephemerides, reports too large to inline). | run registry |

SPICE tools (optional `[spice]` extra)

Install the [spice] extra and seven more tools register, backed by NASA NAIF's CSPICE through spiceypy (they stay hidden otherwise). They furnish kernels into a process-global pool and query whatever the pool holds:

| Tool | What it does | Backed by | | ----------------------- | --------------------------------------------------------- | -------------- | | spice_load_kernel | Furnish a kernel into the pool from a local path or a NAIF https URL (allowlisted, cached); a meta-kernel furnishes all it lists. | spiceypy · NAIF | | spice_list_kernels | List the kernels currently furnished in the pool, optionally filtered by category. | spiceypy | | spice_unload_kernel | Drop a furnished kernel by the name spice_load_kernel returned. | spiceypy | | spice_state | Position / velocity of a target relative to an observer at one or more epochs, from furnished SPK kernels. | spiceypy (SPK) | | spice_frame_transform | Rotate a vector between kernel-defined frames — in particular non-Earth body-fixed frames — or return the rotation matrix. | spiceypy (FK / PCK) | | spice_body_parameters | Read a body's radii, GM, and pole / prime-meridian orientation constants from furnished PCK kernels. | spiceypy (PCK) | | spice_time_convert | Convert between the kernel-defined time systems ET / UTC / SCLK using furnished LSK / SCLK kernels. | spiceypy (LSK / SCLK) |

The kernel model, the NAIF furnish-from-URL allowlist, and the process-global pool's trust boundary are covered on the SPICE integration page.

Visualisation tools (optional `[viz]` extra)

Install the [viz] extra and four more tools register, backed by matplotlib (static PNG plots) and the gmat-czml sibling (CZML export) — they stay hidden otherwise. Each returns its picture as an attachment alongside a numeric summary, so a text-only client still gets the answer:

| Tool | What it does | Backed by | | ------------------- | --------------------------------------------------------- | -------------- | | plot_ground_track | Render a satellite's sub-satellite ground track as a PNG over a lon/lat graticule, with the latitude / longitude extent inline. | matplotlib | | plot_trajectory | Render an orbit or transfer arc as a 2D or 3D PNG about a central body, with arc length and apsides inline. | matplotlib | | plot_porkchop | Render a porkchop C3 contour as a PNG from a full porkchop grid result — no recompute — with the best cell marked. | matplotlib | | czml_trajectory | Export a trajectory as a CZML document for a Cesium 3D client, returned as an embedded resource. | gmat-czml |

The attachment model — additive PNG ImageContent / CZML EmbeddedResource beside the structured summary — and which clients render each kind are covered on the Visualisation page.

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
uv tool install "astrodynamics-mcp[gmat]"    # adds the GMAT mission tools (needs a local GMAT install)
uv tool install "astrodynamics-mcp[spice]"   # adds the SPICE tools (pulls spiceypy / bundled CSPICE)
uv tool install "astrodynamics-mcp[viz]"     # adds the visualisation tools (pulls matplotlib / gmat-czml)

Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "astrodynamics-mcp": {
      "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-mcp": {
      "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

| Client | Transport | Verified | | ---------------------------- | --------------- | --------------------- | | Claude Code | stdio | ✅ Yes | | Cursor | stdio | ✅ Yes | | ChatGPT desktop | stdio | ⏳ Expected to work | | Raw Python (mcp SDK) | stdio | ✅ Yes | | Remote agents | Streamable HTTP | ⏳ Expected to work | | LangGraph / AutoGen / CrewAI | any | ⏳ Expected to work |

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, visualisation, 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.

Privacy

astrodynamics-mcp runs entirely on your own machine and collects nothing — no telemetry, no analytics, no accounts. The only data that leaves your machine is the query parameters a tool sends to the data source it wraps (CelesTrak / JPL Horizons / IERS with no auth, and — only if you configure their credentials — Space-Track and ESA DISCOSweb). Credentials are read from local environment variables or the session _meta block and are sent only to their own service over HTTPS. See the Privacy page for the full breakdown.

License

MIT — see LICENSE.

astrodynamics-mcp