Aerospace MCP - Comprehensive Flight Planning & Space Operations API

🌟 Comprehensive Aerospace Capabilities

✈️

Flight Planning & Airport Resolution

Intelligent airport search, route planning, and aircraft performance modeling with real-time calculations.

28,000+ airports worldwide
Great-circle route calculation
Aircraft-specific performance
Fuel consumption estimation
Multi-leg journey planning

🛰️

Orbital Mechanics & Space Operations

Complete orbital mechanics suite with propagation, rendezvous planning, and trajectory optimization.

Keplerian elements conversion
J2 perturbation modeling
Ground track computation
Hohmann transfers
Lambert problem solving

🌪️

Atmospheric & Aerodynamics

Advanced atmospheric modeling and aerodynamic analysis for comprehensive flight envelope studies.

ISA atmosphere profiles
Wind modeling (logarithmic/power law)
Wing VLM analysis
Airfoil polar generation
Stability derivatives

🚀

Rocket Trajectory Optimization

3DOF trajectory simulation, sizing estimation, and thrust profile optimization for rocket systems.

3DOF numerical integration
Launch angle optimization
Thrust profile optimization
Sizing requirements estimation
Sensitivity analysis

🔧

FastMCP & REST API

Dual interface support with modern FastMCP framework and traditional REST API for maximum flexibility.

FastMCP integration
RESTful API endpoints
Claude Desktop compatible
70% code reduction
Modular tool architecture

🔬

Research & Analysis Tools

Specialized tools for propeller analysis, UAV optimization, and Monte Carlo uncertainty studies.

BEMT propeller analysis
UAV energy optimization
Monte Carlo simulations
Genetic algorithms
Coordinate transformations

💻 Easy Integration

REST API Usage

# Search for airports
# GET /airports/by_city?city=San Jose&country=US
curl "http://localhost:8080/airports/by_city?city=San Jose&country=US"

# Plan a flight
curl -X POST "http://localhost:8080/plan" \
  -H "Content-Type: application/json" \
  -d '{
    "depart_city": "San Jose",
    "arrive_city": "Tokyo",
    "ac_type": "A320",
    "cruise_alt_ft": 35000
  }'

MCP Integration (Anthropic API with Deferred Loading)

Enable deferred tool loading for efficient context usage with 34+ tools:

// Anthropic API with deferred tool loading
{
  "tools": [
    {
      "type": "tool_search_tool_regex_20251119",
      "name": "tool_search_tool_regex"
    },
    {
      "type": "mcp_toolset",
      "mcp_server_name": "aerospace-mcp",
      "default_config": { "defer_loading": true },
      "configs": {
        "search_aerospace_tools": { "defer_loading": false },
        "list_tool_categories": { "defer_loading": false }
      }
    }
  ]
}

Python Client Example

import requests

# Initialize client
base_url = "http://localhost:8080"

# Search airports
response = requests.get(
    f"{base_url}/airports/by_city",
    params={"city": "San Jose", "country": "US"}
)
airports = response.json()

# Plan flight
flight_data = {
    "depart_city": "San Jose",
    "arrive_city": "Tokyo",
    "ac_type": "A320"
}

response = requests.post(f"{base_url}/plan", json=flight_data)
flight_plan = response.json()

🖥️ See It In Action

Experience aerospace-mcp tools through Claude Code. Click the tabs below to see example interactions.

claude — aerospace-mcp

❯ Find airports in Tokyo, Japan

search_airports

query: "Tokyo", country: "JP", query_type: "city"

Found 3 airport(s): • NRT (RJAA) - Narita International Airport City: Tokyo, JP Coordinates: 35.7647, 140.3864 Timezone: Asia/Tokyo • HND (RJTT) - Tokyo Haneda International Airport City: Tokyo, JP Coordinates: 35.5494, 139.7798 Timezone: Asia/Tokyo • NJA (RJTA) - Atsugi Naval Air Facility City: Tokyo, JP Coordinates: 35.4546, 139.4500 Timezone: Asia/Tokyo

❯ What's the IATA code for San Francisco?

search_airports

query: "San Francisco", country: "US"

Found 1 airport(s): • SFO (KSFO) - San Francisco International Airport City: San Francisco, US Coordinates: 37.6213, -122.3790 Timezone: America/Los_Angeles

❯

claude — aerospace-mcp

❯ Simulate a 3DOF trajectory for my sounding rocket: 15kg dry mass, 8kg propellant, 0.12m diameter, 2.0m length, launched at 85 degrees

rocket_3dof_trajectory

rocket_geometry: { dry_mass_kg: 15, propellant_mass_kg: 8, diameter_m: 0.12, length_m: 2.0 }
launch_conditions: { launch_angle_deg: 85 }

3DOF Rocket Trajectory Analysis ================================================== Rocket: 2.0m long, 0.12m dia Mass: 15.0 kg dry + 8.0 kg prop Launch Angle: 85.0° Performance Summary: Max Altitude: 12847 m (12.85 km) Max Velocity: 412.3 m/s (Mach 1.21) Apogee Time: 45.2 s Burnout Time: 8.4 s Max Q: 89.4 kPa Total Impulse: 21000 N⋅s Specific Impulse: 267.8 s Key Trajectory Points: Time (s) Alt (m) Vel (m/s) Accel (m/s²) --------------------------------------------- 0.0 0 0.0 108.7 11.3 1847 312.4 -9.8 22.5 8234 198.2 -9.8 33.8 11892 89.1 -9.8 45.0 12847 0.0 -9.8

❯ Now optimize the launch angle for maximum altitude

optimize_launch_angle

rocket_geometry: { dry_mass_kg: 15, propellant_mass_kg: 8, ... }
optimize_for: "altitude", angle_bounds_deg: [75, 90]

{ "optimal_angle_deg": 87.3, "objective": "altitude", "max_altitude_m": 13102.4, "max_velocity_ms": 418.7, "apogee_time_s": 46.8, "iterations": 12, "convergence": "success" }

❯

claude — aerospace-mcp

❯ Calculate a Hohmann transfer from a 400km LEO orbit to geostationary orbit at 35,786km altitude

hohmann_transfer

r1_m: 6778000 (400km + 6378km Earth radius)
r2_m: 42164000 (35786km + 6378km Earth radius)

{ "delta_v1_ms": 2397.51, "delta_v2_ms": 1456.50, "total_delta_v_ms": 3854.01, "transfer_time_s": 19048.40, "transfer_time_h": 5.29, "transfer_semi_major_axis_m": 24471000 }

The transfer requires 3,854 m/s total Δv: first burn of 2,398 m/s at perigee raises apogee to GEO, then 1,457 m/s at apogee circularizes the orbit. Transfer time is about 5.3 hours.

❯ What about a transfer to lunar distance at 384,400km?

hohmann_transfer

r1_m: 6778000, r2_m: 384400000 (Earth-centered distance)

{ "delta_v1_ms": 3082.09, "delta_v2_ms": 828.74, "total_delta_v_ms": 3910.82, "transfer_time_s": 430416.00, "transfer_time_h": 119.56, "transfer_semi_major_axis_m": 195589000 }

A Hohmann transfer to lunar distance requires 3,082 m/s at perigee, with a ~5 day coast. Note: real lunar missions use 3-body trajectories (~3 days) and require separate LOI burns relative to the Moon.

❯

🚀 Quick Start

⚡ UV (Recommended)

Fastest Python package manager with excellent dependency resolution.

# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and setup
git clone https://github.com/cheesejaguar/aerospace-mcp.git
cd aerospace-mcp
uv venv && source .venv/bin/activate
uv sync

# Run HTTP server
uvicorn main:app --reload --host 0.0.0.0 --port 8080

🐳 Docker

Containerized deployment for consistent environments.

# Clone and build
git clone https://github.com/cheesejaguar/aerospace-mcp.git
cd aerospace-mcp

# Build and run
docker build -t aerospace-mcp .
docker run -p 8080:8080 aerospace-mcp

# Test the API
curl "http://localhost:8080/health"

🤖 Claude Code MCP

Add aerospace-mcp to your Claude Code settings for full tool access.

// Add to ~/.claude/settings.json
{
  "mcpServers": {
    "aerospace-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/aerospace-mcp",
        "run",
        "aerospace-mcp"
      ]
    }
  }
}

⚠️ IMPORTANT SAFETY NOTICE

THIS SOFTWARE IS FOR EDUCATIONAL, RESEARCH, AND DEVELOPMENT PURPOSES ONLY

• NOT FOR REAL NAVIGATION - Do not use for actual flight planning or navigation

• NOT CERTIFIED - This system is not certified by any aviation authority

• ESTIMATES ONLY - Performance calculations are theoretical estimates

• NO LIABILITY - Authors assume no responsibility for any consequences of use

For real flight planning, always use certified aviation software and consult official sources.