Aerospace MCP

Instructions

Implementation Reference

• aerospace_mcp/tools/agents.py:170-248 (handler)

  The core handler function implementing the 'format_data_for_tool' logic. It uses an LLM (GPT-5-Medium via LiteLLM) to format user requirements and raw data into valid JSON parameters for the specified aerospace tool, referencing pre-defined tool schemas.
  def format_data_for_tool( tool_name: str, user_requirements: str, raw_data: str = "" ) -> str: """ Help format data in the correct format for a specific aerospace-mcp tool. Uses GPT-5-Medium to analyze the user's requirements and raw data, then provides the correctly formatted parameters for the specified tool. Args: tool_name: Name of the aerospace-mcp tool to format data for user_requirements: Description of what the user wants to accomplish raw_data: Any raw data that needs to be formatted (optional) Returns: Formatted JSON string with the correct parameters for the tool """ # 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." # Find the tool reference first tool_ref = None for tool in AEROSPACE_TOOLS: if tool.name == tool_name: tool_ref = tool break if not tool_ref: available_tools = [t.name for t in AEROSPACE_TOOLS] return f"Error: Tool '{tool_name}' not found. Available tools: {', '.join(available_tools)}" if "OPENAI_API_KEY" not in os.environ: return "Error: OPENAI_API_KEY environment variable not set. Cannot use agent tools." # Build the prompt for GPT-5-Medium system_prompt = f"""You are a data formatting assistant for aerospace-mcp tools. Your job is to help format data correctly for the '{tool_name}' tool. Tool Information: - Name: {tool_ref.name} - Description: {tool_ref.description} - Parameters: {json.dumps(tool_ref.parameters, indent=2)} - Examples: {json.dumps(tool_ref.examples, indent=2)} User Requirements: {user_requirements} Raw Data (if provided): {raw_data} Please provide ONLY a valid JSON object with the correctly formatted parameters for this tool. Do not include any explanation or additional text - just the JSON object that can be directly used as input to the tool. If the user's requirements are unclear or insufficient data is provided, return a JSON object with an "error" field explaining what additional information is needed.""" try: # Call GPT-5-Medium via LiteLLM response = litellm.completion( model="gpt-5-medium", messages=[ {"role": "system", "content": system_prompt}, { "role": "user", "content": f"Format data for {tool_name}: {user_requirements}", }, ], temperature=0.1, max_tokens=1000, ) formatted_result = response.choices[0].message.content.strip() # Validate it's valid JSON try: json.loads(formatted_result) return formatted_result except json.JSONDecodeError: return f'{{"error": "Failed to generate valid JSON format. Raw response: {formatted_result}"}}' except Exception as e: return f'{{"error": "Failed to format data: {str(e)}"}}'
• aerospace_mcp/fastmcp_server.py:132-132 (registration)

  Registration of the 'format_data_for_tool' tool with the FastMCP server instance.
  mcp.tool(format_data_for_tool)
• aerospace_mcp/tools/agents.py:38-167 (helper)

  Pre-defined list of all available aerospace tools with their schemas, descriptions, parameters, and examples. Used by format_data_for_tool to provide context to the LLM for accurate data formatting.
  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)' ], ), ]
• aerospace_mcp/tools/agents.py:28-35 (schema)

  Pydantic model defining the schema structure for tool references used internally by the handler.
  class ToolReference(BaseModel): """Reference to an aerospace-mcp tool with its schema.""" name: str description: str parameters: dict[str, Any] examples: list[str] = []
• aerospace_mcp/fastmcp_server.py:26-27 (registration)

  Import statement bringing the tool handler into the server module for registration.
  from .tools.agents import ( format_data_for_tool,