Model Context Protocol server

Aerospace engineering tools, wired into your AI agent

An open-source MCP server exposing 47 tools across flight planning, orbital mechanics, rocket trajectories, aerodynamics, and trajectory optimization — callable from Claude and any MCP-compatible client.

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GC-PLOT · WGS-84 PLAN_FLIGHT

RouteSJC → NRT

Great circle8,295 km

EngineOpenAP · A359

47
aerospace tools

11
engineering domains

7,861
airports (IATA)

37
aircraft models

Capabilities

Eleven domains, one protocol

Every tool is a typed Python function with an auto-generated MCP schema — discoverable at runtime through the built-in tool search, and callable from the CLI without an MCP client.

Flight planning

Great-circle routes on WGS-84, multi-leg journeys, wind-aware ETA and fuel from OpenAP performance models.

plan_flight · plan_multi_leg_flight · search_airports

Orbital mechanics

Kepler element conversions, J2 propagation, ground tracks, Hohmann transfers, rendezvous, and Lambert solvers.

propagate_orbit_j2 · hohmann_transfer · lambert_problem_solver

Rockets

3-DOF trajectory simulation with ISA atmosphere, vehicle sizing estimates, and launch-angle optimization.

rocket_3dof_trajectory · optimize_launch_angle

Aerodynamics

Wing VLM analysis, airfoil polars, stability derivatives, and propeller BEMT with UAV energy estimates.

wing_vlm_analysis · airfoil_polar_analysis · propeller_bemt_analysis

Atmosphere & frames

ISA atmosphere profiles, wind models, and ECEF/ECI/geodetic transforms with GMST Earth-rotation handling.

get_atmosphere_profile · transform_frames · geodetic_to_ecef

Aircraft performance

Density altitude, V-speeds, weight & balance, takeoff and landing distances, fuel reserves, and unit conversion.

takeoff_performance · weight_and_balance · convert_units

Optimization & GNC

Genetic algorithms, particle swarm, Monte Carlo uncertainty, porkchop plots, Kalman filtering, and LQR design.

porkchop_plot_analysis · kalman_filter_state_estimation

Tool discovery

Search tools by name, keyword, or safe regex — built for Anthropic's deferred tool loading so agents keep context lean.

search_aerospace_tools · list_tool_categories

Integration

Call it your way

The same core logic is exposed three ways: an MCP server for AI agents, a FastAPI HTTP service, and a direct CLI.

{
  "mcpServers": {
    "aerospace": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/aerospace-mcp",
        "run",
        "aerospace-mcp"
      ]
    }
  }
}

# Search airports by city
curl "http://localhost:8080/airports/by_city?city=San%20Jose&country=US"

# Plan a flight with performance estimates
curl -X POST "http://localhost:8080/plan" \
  -H "Content-Type: application/json" \
  -d '{
    "depart_city": "San Jose",
    "arrive_city": "Tokyo",
    "ac_type": "A359",
    "cruise_alt_ft": 35000
  }'

import requests

plan = requests.post("http://localhost:8080/plan", json={
    "depart_city": "San Jose",
    "arrive_city": "Tokyo",
    "ac_type": "A359",
}).json()

est = plan["estimates"]["block"]
print(f"{plan['distance_km']:.0f} km, "
      f"{est['time_min']/60:.1f} h, {est['fuel_kg']:.0f} kg fuel")

# Discover tools
aerospace-mcp-cli search "orbit transfer"

# Convert units
aerospace-mcp-cli run convert_units --value 100 --from_unit kts --to_unit mps

# Plan a multi-leg journey
aerospace-mcp-cli run plan_multi_leg_flight \
  --waypoints '[{"iata":"SJC"},{"iata":"NRT"},{"iata":"SIN"}]' \
  --aircraft '{"ac_type":"A359"}'

On the wire

What an agent sees

Representative tool calls and their responses.

▸ search_airports {"query": "SJC"}
Found 1 airport(s):

• SJC (KSJC) - Norman Y. Mineta San Jose International Airport
  City: San Jose, US
  Coordinates: 37.3626, -121.9290
  Timezone: America/Los_Angeles

▸ optimize_launch_angle {"target_range_m": 2000, "dry_mass_kg": 20, "propellant_mass_kg": 80}
Launch Angle Optimization Results:

Optimal launch angle: 47.2°
Achieved range:       2,003 m
Apogee altitude:      1,247 m
Flight time:          32.4 s
Iterations:           14

▸ hohmann_transfer {"r1_km": 6778, "r2_km": 42164}
Hohmann Transfer: LEO (400 km) → GEO

Delta-v burn 1:    2.44 km/s
Delta-v burn 2:    1.47 km/s
Total delta-v:     3.91 km/s
Transfer time:     5.26 h
Transfer SMA:      24,471 km

Quick start

Running in under a minute

UV (recommended)

git clone https://github.com/cheesejaguar/aerospace-mcp
cd aerospace-mcp
uv sync

# MCP server (stdio)
uv run aerospace-mcp

# or the HTTP API on :8080
uv run aerospace-mcp-http

Docker

docker build -t aerospace-mcp .
docker run -p 8080:8080 aerospace-mcp

# verify
curl http://localhost:8080/health

Claude Code

claude mcp add aerospace -- \
  uv --directory /path/to/aerospace-mcp \
  run aerospace-mcp

# then, in a session:
# "Plan a flight from SJC to Tokyo on an A359"

Research and education only.

Outputs are simplified engineering estimates with no certification, no weather data, and no airspace awareness. Never use this software for real navigation, flight planning, or aircraft and spacecraft operations.