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Physics MCP Server

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calculate_drag_force

Calculate the drag force opposing motion of an object through a fluid using velocity, cross-sectional area, fluid density, and drag coefficient. Also returns Reynolds number.

Instructions

Calculate drag force for an object moving through a fluid.

The drag force opposes motion and is given by:
    F_drag = 0.5 * ρ * v² * C_d * A

Common drag coefficients:
    - Sphere: 0.47
    - Streamlined shape: 0.04
    - Flat plate (perpendicular): 1.28
    - Human (standing): 1.0-1.3
    - Car: 0.25-0.35

Args:
    velocity: Velocity vector [x, y, z] in m/s (or JSON string)
    cross_sectional_area: Area perpendicular to flow in m²
    fluid_density: Fluid density in kg/m³ (water=1000, air=1.225)
    drag_coefficient: Drag coefficient (default 0.47 for sphere)
    viscosity: Dynamic viscosity in Pa·s (water=1.002e-3, air=1.825e-5, oil=0.1).
        If not provided, estimated from fluid_density for Reynolds number calculation.

Returns:
    Drag force vector, magnitude, and Reynolds number

Example - Ball falling through water:
    result = await calculate_drag_force(
        velocity=[0, -5.0, 0],
        cross_sectional_area=0.00785,  # π * (0.05m)² for 10cm diameter
        fluid_density=1000,  # water
        drag_coefficient=0.47,
        viscosity=1.002e-3  # water viscosity for accurate Reynolds number
    )
    # Returns upward drag force opposing downward motion

Example - Ball falling through motor oil:
    result = await calculate_drag_force(
        velocity=[0, -2.0, 0],
        cross_sectional_area=0.00785,
        fluid_density=900,  # oil
        drag_coefficient=0.47,
        viscosity=0.1  # motor oil is much more viscous
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
velocityYes
cross_sectional_areaYes
fluid_densityYes
drag_coefficientNo
viscosityNo

Implementation Reference

  • MCP @tool-decorated async function that exposes calculate_drag_force as a tool. Parses velocity if needed, creates a DragForceRequest, delegates to fluid_module.calculate_drag_force(), and returns the response dict.
    @tool  # type: ignore[arg-type]
    async def calculate_drag_force(
        velocity: Union[list[float], str],
        cross_sectional_area: float,
        fluid_density: float,
        drag_coefficient: float = 0.47,
        viscosity: Optional[float] = None,
    ) -> dict:
        """Calculate drag force for an object moving through a fluid.
    
        The drag force opposes motion and is given by:
            F_drag = 0.5 * ρ * v² * C_d * A
    
        Common drag coefficients:
            - Sphere: 0.47
            - Streamlined shape: 0.04
            - Flat plate (perpendicular): 1.28
            - Human (standing): 1.0-1.3
            - Car: 0.25-0.35
    
        Args:
            velocity: Velocity vector [x, y, z] in m/s (or JSON string)
            cross_sectional_area: Area perpendicular to flow in m²
            fluid_density: Fluid density in kg/m³ (water=1000, air=1.225)
            drag_coefficient: Drag coefficient (default 0.47 for sphere)
            viscosity: Dynamic viscosity in Pa·s (water=1.002e-3, air=1.825e-5, oil=0.1).
                If not provided, estimated from fluid_density for Reynolds number calculation.
    
        Returns:
            Drag force vector, magnitude, and Reynolds number
    
        Example - Ball falling through water:
            result = await calculate_drag_force(
                velocity=[0, -5.0, 0],
                cross_sectional_area=0.00785,  # π * (0.05m)² for 10cm diameter
                fluid_density=1000,  # water
                drag_coefficient=0.47,
                viscosity=1.002e-3  # water viscosity for accurate Reynolds number
            )
            # Returns upward drag force opposing downward motion
    
        Example - Ball falling through motor oil:
            result = await calculate_drag_force(
                velocity=[0, -2.0, 0],
                cross_sectional_area=0.00785,
                fluid_density=900,  # oil
                drag_coefficient=0.47,
                viscosity=0.1  # motor oil is much more viscous
            )
        """
        # Parse velocity if string
        parsed_velocity = json.loads(velocity) if isinstance(velocity, str) else velocity
    
        request = DragForceRequest(
            velocity=parsed_velocity,
            cross_sectional_area=cross_sectional_area,
            fluid_density=fluid_density,
            drag_coefficient=drag_coefficient,
            viscosity=viscosity,
        )
    
        response = fluid_module.calculate_drag_force(request)
        return response.model_dump()
Behavior4/5

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

No annotations provided, but the description discloses the formula, return values (drag force vector, magnitude, Reynolds number), and behavioral details like viscosity estimation when omitted. No contradictions.

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 front-loaded with the formula and purpose, and provides structured examples. It is detailed but somewhat long; every sentence contributes value, though minor redundancy exists.

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

Completeness4/5

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

Despite lacking an output schema, the description explains return values and covers all parameters comprehensively. Examples illustrate usage in different scenarios, making the tool fully understandable.

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

Parameters5/5

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

Input schema has 0% description coverage, but the description compensates thoroughly by explaining each parameter with units, defaults, and typical values (e.g., 'velocity: Velocity vector [x, y, z] in m/s').

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

Purpose5/5

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

The description clearly states 'Calculate drag force for an object moving through a fluid', uses a specific verb-resource pair, and the formula and context distinguish it from siblings like lift force or buoyancy.

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

Usage Guidelines3/5

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

The description includes common drag coefficients and examples for different fluids, which imply typical use cases, but it does not explicitly state when to use this tool over alternatives or when not to use it.

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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