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get_propeller_database

Access propeller geometric and performance data for aircraft flight planning and performance estimation within the Aerospace MCP server.

Instructions

Get available propeller database with geometric and performance data.

Returns: JSON string with propeller database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main MCP tool handler function. Imports PROPELLER_DATABASE from integrations/propellers.py and returns it as a JSON-formatted string. Includes error handling for missing dependencies.
    def get_propeller_database() -> str:
        """Get available propeller database with geometric and performance data.
    
        Returns:
            JSON string with propeller database
        """
        try:
            from ..integrations.propellers import PROPELLER_DATABASE
    
            return json.dumps(PROPELLER_DATABASE, indent=2)
    
        except ImportError:
            return "Propeller database not available"
        except Exception as e:
            logger.error(f"Propeller database error: {str(e)}", exc_info=True)
            return f"Propeller database error: {str(e)}"
  • Registers the get_propeller_database tool function with the FastMCP server instance.
    mcp.tool(get_propeller_database)
  • Static PROPELLER_DATABASE dictionary containing specifications for common propellers (APC and Multistar models), directly used by the tool handler.
    PROPELLER_DATABASE = {
        "APC_10x7": {
            "diameter_m": 0.254,  # 10 inches
            "pitch_m": 0.178,  # 7 inches
            "num_blades": 2,
            "activity_factor": 100,
            "cl_design": 0.5,
            "cd_design": 0.02,
            "efficiency_max": 0.82,
        },
        "APC_12x8": {
            "diameter_m": 0.305,  # 12 inches
            "pitch_m": 0.203,  # 8 inches
            "num_blades": 2,
            "activity_factor": 110,
            "cl_design": 0.5,
            "cd_design": 0.02,
            "efficiency_max": 0.84,
        },
        "APC_15x10": {
            "diameter_m": 0.381,  # 15 inches
            "pitch_m": 0.254,  # 10 inches
            "num_blades": 2,
            "activity_factor": 120,
            "cl_design": 0.5,
            "cd_design": 0.02,
            "efficiency_max": 0.85,
        },
        "MULTISTAR_8045": {
            "diameter_m": 0.203,  # 8 inches
            "pitch_m": 0.114,  # 4.5 inches
            "num_blades": 3,
            "activity_factor": 90,
            "cl_design": 0.6,
            "cd_design": 0.025,
            "efficiency_max": 0.75,
        },
    }
  • Imports the get_propeller_database tool from the tools.propellers module for registration.
    from .tools.propellers import (
        get_propeller_database,
        propeller_bemt_analysis,
        uav_energy_estimate,
    )
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a JSON string but does not describe other behavioral traits such as data format, size, or access constraints. This is inadequate for a tool with no annotation coverage, as it misses details like potential rate limits or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, with the main purpose stated in the first sentence and a brief note on returns. Both sentences earn their place by clarifying the tool's function and output format. However, it could be slightly more structured by integrating the return information into the purpose statement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no annotations, but with an output schema), the description is minimally adequate. It explains what the tool does and the return format, but lacks context on usage, behavioral details, or how it fits with sibling tools. The output schema reduces the need to explain return values, but overall completeness is limited.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description does not add parameter details, which is appropriate. The baseline for 0 parameters is 4, as the description does not need to compensate for any schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get available propeller database with geometric and performance data.' This specifies the verb ('Get') and resource ('propeller database') with additional details about content ('geometric and performance data'). However, it does not explicitly differentiate from sibling tools like 'get_airfoil_database' or 'get_aircraft_performance', which lowers the score from a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It lacks context about prerequisites, typical use cases, or comparisons to sibling tools such as 'propeller_bemt_analysis' or 'get_airfoil_database'. This leaves the agent without explicit or implied usage instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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