Aerospace MCP

Instructions

Implementation Reference

• aerospace_mcp/tools/agents.py:250-325 (handler)

  The core handler function for the 'select_aerospace_tool' MCP tool. It uses GPT-5-Medium (via LiteLLM) to analyze the user task and context, then recommends the best aerospace-mcp tool(s) with structured guidance.
  def select_aerospace_tool(user_task: str, user_context: str = "") -> str: """ Help select the most appropriate aerospace-mcp tool for a given task. Uses GPT-5-Medium to analyze the user's task and recommend the best tool(s) along with guidance on how to use them. Args: user_task: Description of what the user wants to accomplish user_context: Additional context about the user's situation (optional) Returns: Recommendation with tool name(s) and usage guidance """ # Check if LLM tools are enabled if not LLM_TOOLS_ENABLED: return "Error: LLM agent tools are disabled. Set LLM_TOOLS_ENABLED=true to enable them." if "OPENAI_API_KEY" not in os.environ: return "Error: OPENAI_API_KEY environment variable not set. Cannot use agent tools." # Build comprehensive tool catalog for the AI tools_catalog = [] for tool in AEROSPACE_TOOLS: tools_catalog.append( { "name": tool.name, "description": tool.description, "parameters": tool.parameters, "examples": tool.examples, } ) system_prompt = f"""You are an aerospace engineering assistant specialized in helping users select the right tools for their aerospace calculations and analysis tasks. Available Aerospace Tools: {json.dumps(tools_catalog, indent=2)} User Task: {user_task} User Context: {user_context} Your job is to: 1. Analyze the user's task and context 2. Recommend the most appropriate tool(s) from the available aerospace tools 3. Provide clear guidance on how to use the recommended tool(s) 4. If multiple tools are needed, explain the workflow and order of operations 5. Highlight any prerequisites, limitations, or important considerations Respond in a clear, structured format with: - PRIMARY_TOOL: The main tool to use - SECONDARY_TOOLS: Any additional tools needed (if applicable) - WORKFLOW: Step-by-step guidance - CONSIDERATIONS: Important notes, limitations, or prerequisites - EXAMPLE: A concrete example of how to use the tool(s) for this task If the user's task cannot be accomplished with the available tools, clearly explain what's missing and suggest alternatives.""" try: # Call GPT-5-Medium via LiteLLM response = litellm.completion( model="gpt-5-medium", messages=[ {"role": "system", "content": system_prompt}, { "role": "user", "content": f"Help me with this task: {user_task}\n\nContext: {user_context}", }, ], temperature=0.2, max_tokens=1500, ) return response.choices[0].message.content.strip() except Exception as e: return f"Error: Failed to select appropriate tool: {str(e)}"
• aerospace_mcp/fastmcp_server.py:133-133 (registration)

  Registration of the select_aerospace_tool handler as an MCP tool in the FastMCP server.
  mcp.tool(select_aerospace_tool)
• aerospace_mcp/tools/agents.py:38-167 (helper)

  List of all available aerospace-mcp tools with their schemas (parameters and examples), used by select_aerospace_tool to build the tool catalog for LLM prompting.
  AEROSPACE_TOOLS = [ ToolReference( name="search_airports", description="Search for airports by IATA code or city name", parameters={ "query": "str - IATA code (e.g., 'SJC') or city name (e.g., 'San Jose')", "country": "str | None - Optional ISO country code filter (e.g., 'US', 'JP')", "query_type": "Literal['iata', 'city', 'auto'] - Type of query, defaults to 'auto'", }, examples=[ 'search_airports("SFO")', 'search_airports("London", "GB")', 'search_airports("Tokyo")', ], ), ToolReference( name="plan_flight", description="Generate complete flight plan between airports", parameters={ "departure": "dict - Airport info with city, iata (optional), country (optional)", "arrival": "dict - Airport info with city, iata (optional), country (optional)", "aircraft": "dict - Aircraft config with type, cruise_alt_ft, mass_kg (optional)", "route_options": "dict - Route config with step_km (optional, default 25.0)", }, examples=[ 'plan_flight({"city": "San Francisco"}, {"city": "New York"}, {"type": "A320", "cruise_alt_ft": 37000}, {})', 'plan_flight({"city": "London", "iata": "LHR"}, {"city": "Dubai", "iata": "DXB"}, {"type": "B777", "cruise_alt_ft": 39000, "mass_kg": 220000}, {"step_km": 50.0})', ], ), ToolReference( name="calculate_distance", description="Calculate great-circle distance between airports", parameters={ "origin": "dict - Origin airport with city and optional iata/country", "destination": "dict - Destination airport with city and optional iata/country", "step_km": "float - Optional step size for route polyline generation (default 25.0)", }, examples=[ 'calculate_distance({"city": "New York"}, {"city": "Los Angeles"})', 'calculate_distance({"city": "Paris", "iata": "CDG"}, {"city": "Tokyo", "iata": "NRT"}, 100.0)', ], ), ToolReference( name="get_aircraft_performance", description="Get performance estimates for aircraft", parameters={ "aircraft_type": "str - Aircraft type code (e.g., 'A320', 'B737', 'B777')", "distance_km": "float - Flight distance in kilometers", "cruise_altitude_ft": "float - Cruise altitude in feet (optional, default 35000)", "mass_kg": "float - Aircraft mass in kg (optional, uses 85% MTOW if not provided)", }, examples=[ 'get_aircraft_performance("A320", 2500.0, 37000)', 'get_aircraft_performance("B777", 5500.0, 39000, 250000)', ], ), ToolReference( name="get_atmosphere_profile", description="Calculate atmospheric conditions at various altitudes", parameters={ "altitudes_m": "List[float] - List of altitudes in meters", "model_type": "Literal['isa', 'enhanced'] - Atmospheric model type (default 'isa')", }, examples=[ "get_atmosphere_profile([0, 1000, 5000, 10000])", 'get_atmosphere_profile([0, 2000, 4000, 6000, 8000, 10000], "enhanced")', ], ), ToolReference( name="wind_model_simple", description="Calculate wind profiles at various altitudes", parameters={ "altitudes_m": "List[float] - List of altitudes in meters", "surface_wind_mps": "float - Surface wind speed in m/s", "model": "Literal['logarithmic', 'power_law'] - Wind profile model (default 'logarithmic')", "surface_roughness_m": "float - Surface roughness in meters (default 0.1)", }, examples=[ "wind_model_simple([0, 100, 500, 1000], 10.0)", 'wind_model_simple([0, 200, 1000, 3000], 15.0, "power_law", 0.05)', ], ), ToolReference( name="elements_to_state_vector", description="Convert orbital elements to state vector", parameters={ "elements": "dict - Orbital elements with semi_major_axis_m, eccentricity, inclination_deg, raan_deg, arg_periapsis_deg, true_anomaly_deg, epoch_utc" }, examples=[ 'elements_to_state_vector({"semi_major_axis_m": 6793000, "eccentricity": 0.001, "inclination_deg": 51.6, "raan_deg": 0.0, "arg_periapsis_deg": 0.0, "true_anomaly_deg": 0.0, "epoch_utc": "2024-01-01T12:00:00"})' ], ), ToolReference( name="propagate_orbit_j2", description="Propagate satellite orbit with J2 perturbations", parameters={ "initial_state": "dict - Initial orbital elements or state vector", "time_span_s": "float - Propagation time span in seconds", "time_step_s": "float - Time step for propagation in seconds (default 300)", }, examples=[ 'propagate_orbit_j2({"semi_major_axis_m": 6793000, "eccentricity": 0.001, "inclination_deg": 51.6, "raan_deg": 0.0, "arg_periapsis_deg": 0.0, "true_anomaly_deg": 0.0, "epoch_utc": "2024-01-01T12:00:00"}, 86400, 600)' ], ), ToolReference( name="hohmann_transfer", description="Calculate Hohmann transfer orbit between two circular orbits", parameters={ "r1_m": "float - Initial orbit radius in meters", "r2_m": "float - Final orbit radius in meters", }, examples=[ "hohmann_transfer(6778000, 42164000)", # LEO to GEO "hohmann_transfer(6578000, 6793000)", # Lower LEO to ISS altitude ], ), ToolReference( name="rocket_3dof_trajectory", description="Simulate 3DOF rocket trajectory with atmospheric effects", parameters={ "geometry": "dict - Rocket geometry with mass_kg, thrust_n, burn_time_s, drag_coeff, reference_area_m2", "dt_s": "float - Time step in seconds (default 0.1)", "max_time_s": "float - Maximum simulation time in seconds (default 300)", "launch_angle_deg": "float - Launch angle in degrees from vertical (default 0)", }, examples=[ 'rocket_3dof_trajectory({"mass_kg": 500, "thrust_n": 8000, "burn_time_s": 60, "drag_coeff": 0.3, "reference_area_m2": 0.5}, 0.1, 300, 15)' ], ), ]