Aerospace MCP

Instructions

Implementation Reference

• aerospace_mcp/fastmcp_server.py:48-55 (registration)

  Import of the optimize_thrust_profile tool handler from tools/optimization.py
  from .tools.optimization import ( genetic_algorithm_optimization, monte_carlo_uncertainty_analysis, optimize_thrust_profile, particle_swarm_optimization, porkchop_plot_analysis, trajectory_sensitivity_analysis, )
• aerospace_mcp/fastmcp_server.py:124-124 (registration)

  MCP registration of the optimize_thrust_profile tool handler
  mcp.tool(optimize_thrust_profile)
• aerospace_mcp/tools/optimization.py:10-65 (handler)

  MCP tool handler: wrapper that converts inputs to RocketGeometry, delegates to trajopt implementation, formats results as JSON string
  def optimize_thrust_profile( rocket_geometry: dict, burn_time_s: float, total_impulse_target: float, n_segments: int = 5, objective: Literal[ "max_altitude", "min_max_q", "min_gravity_loss" ] = "max_altitude", ) -> str: """Optimize rocket thrust profile for better performance using trajectory optimization. Args: rocket_geometry: Rocket geometry parameters burn_time_s: Burn time in seconds total_impulse_target: Target total impulse in N⋅s n_segments: Number of thrust segments objective: Optimization objective Returns: JSON string with optimized thrust profile """ try: from ..integrations.trajopt import ( RocketGeometry, ) from ..integrations.trajopt import ( optimize_thrust_profile as _optimize, ) geometry = RocketGeometry(**rocket_geometry) result = _optimize( geometry, burn_time_s, total_impulse_target, n_segments, objective ) return json.dumps( { "optimization_result": result.optimization_result, "optimal_objective": result.optimal_objective, "optimal_parameters": result.optimal_parameters, "performance": { "max_altitude_m": result.performance.max_altitude_m, "max_velocity_ms": result.performance.max_velocity_ms, "total_impulse_ns": result.performance.total_impulse_ns, "apogee_time_s": result.performance.apogee_time_s, }, }, indent=2, ) except ImportError: return "Trajectory optimization not available - install optimization packages" except Exception as e: logger.error(f"Thrust profile optimization error: {str(e)}", exc_info=True) return f"Thrust profile optimization error: {str(e)}"
• aerospace_mcp/integrations/trajopt.py:207-353 (helper)

  Core helper implementation: optimizes thrust multipliers using simple gradient descent, normalizes thrust curve to target impulse, simulates trajectory with rocket_3dof_trajectory, returns structured TrajectoryOptimizationResult
  def optimize_thrust_profile( geometry: RocketGeometry, burn_time_s: float, total_impulse_target: float, n_segments: int = 5, objective: str = "max_altitude", ) -> TrajectoryOptimizationResult: """ Optimize thrust profile for better performance. Args: geometry: Base rocket geometry burn_time_s: Total burn time total_impulse_target: Target total impulse n_segments: Number of thrust profile segments objective: "max_altitude", "min_max_q", or "min_gravity_loss" Returns: Optimization result """ # Create initial thrust profile (constant thrust) avg_thrust = total_impulse_target / burn_time_s segment_time = burn_time_s / n_segments # Initial parameters: thrust multipliers for each segment initial_multipliers = [1.0] * n_segments multiplier_bounds = [(0.1, 3.0)] * n_segments # 10% to 300% of average def objective_function(multipliers: list[float]) -> float: """Objective function for thrust profile optimization.""" try: # Create thrust curve from multipliers thrust_curve = [] for i, mult in enumerate(multipliers): time_start = i * segment_time time_end = (i + 1) * segment_time thrust_level = avg_thrust * mult thrust_curve.append([time_start, thrust_level]) if i == len(multipliers) - 1: # Last segment thrust_curve.append([time_end, 0.0]) # End thrust # Normalize to maintain total impulse current_impulse = sum( avg_thrust * mult * segment_time for mult in multipliers ) if current_impulse <= 0: return float("inf") scale_factor = total_impulse_target / current_impulse thrust_curve = [[t, thrust * scale_factor] for t, thrust in thrust_curve] # Update geometry with new thrust curve opt_geometry = RocketGeometry( dry_mass_kg=geometry.dry_mass_kg, propellant_mass_kg=geometry.propellant_mass_kg, diameter_m=geometry.diameter_m, length_m=geometry.length_m, cd=geometry.cd, thrust_curve=thrust_curve, ) # Run trajectory simulation with reasonable parameters trajectory = rocket_3dof_trajectory( opt_geometry, dt_s=0.2, max_time_s=150.0 ) if not trajectory: return 1e6 # Large penalty instead of inf performance = analyze_rocket_performance(trajectory) if objective == "max_altitude": # Return negative altitude for minimization, but ensure it's not zero result = -max( 1.0, performance.max_altitude_m ) # At least -1 to avoid zero return result elif objective == "min_max_q": return performance.max_q_pa elif objective == "min_gravity_loss": # Estimate gravity loss (simplified) gravity_loss = 9.80665 * performance.burnout_time_s return gravity_loss else: return 1e6 except Exception: return 1e6 # Large penalty for failed cases # Optimize using gradient descent optimal_multipliers, optimal_value, iterations, converged = simple_gradient_descent( objective_function, initial_multipliers, multiplier_bounds, learning_rate=0.05, tolerance=1e-3, max_iterations=100, ) # Generate final thrust curve and trajectory final_thrust_curve = [] for i, mult in enumerate(optimal_multipliers): time_start = i * segment_time time_end = (i + 1) * segment_time thrust_level = avg_thrust * mult final_thrust_curve.append([time_start, thrust_level]) if i == len(optimal_multipliers) - 1: final_thrust_curve.append([time_end, 0.0]) # Normalize final curve current_impulse = sum( avg_thrust * mult * segment_time for mult in optimal_multipliers ) scale_factor = total_impulse_target / current_impulse final_thrust_curve = [ [t, thrust * scale_factor] for t, thrust in final_thrust_curve ] # Generate final trajectory final_geometry = RocketGeometry( dry_mass_kg=geometry.dry_mass_kg, propellant_mass_kg=geometry.propellant_mass_kg, diameter_m=geometry.diameter_m, length_m=geometry.length_m, cd=geometry.cd, thrust_curve=final_thrust_curve, ) final_trajectory = rocket_3dof_trajectory(final_geometry) final_performance = analyze_rocket_performance(final_trajectory) # Prepare optimal parameters optimal_params = { f"thrust_mult_seg_{i + 1}": mult for i, mult in enumerate(optimal_multipliers) } optimal_params["thrust_curve"] = final_thrust_curve return TrajectoryOptimizationResult( optimal_parameters=optimal_params, optimal_objective=( -optimal_value if objective == "max_altitude" else optimal_value ), iterations=iterations, converged=converged, trajectory_points=final_trajectory, performance=final_performance, )