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IBM

Physics MCP Server

by IBM

calculate_centripetal_force

Calculate the centripetal force and acceleration required to keep an object moving in a circular path. Use for car turns, satellite orbits, and centrifuges.

Instructions

Calculate centripetal force: F_c = m v² / r.

Force required to keep an object moving in a circle.
Always points toward the center of the circular path.

Args:
    mass: Mass in kg
    velocity: Speed (velocity magnitude) in m/s
    radius: Radius of circular path in meters

Returns:
    Dict containing:
        - centripetal_force: F_c in Newtons
        - centripetal_acceleration: a_c in m/s²

Tips for LLMs:
    - Not a new force - it's the net inward force (tension, friction, gravity)
    - Faster speed → much more force needed (v² relationship)
    - Tighter turn → more force needed
    - Use for: car turns, satellite orbits, centrifuges

Example - Car turning:
    result = await calculate_centripetal_force(
        mass=1500,  # kg
        velocity=20,  # m/s (72 km/h)
        radius=50  # meter turn radius
    )
    # F_c = 12000 N (provided by friction between tires and road)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
massYes
velocityYes
radiusYes

Implementation Reference

  • Core calculation function: computes centripetal force (F_c = m * v² / r) and centripetal acceleration (a_c = v² / r). Takes a CentripetalForceRequest and returns a CentripetalForceResponse.
    def calculate_centripetal_force(request: CentripetalForceRequest) -> CentripetalForceResponse:
        """Calculate centripetal force: F_c = m * v² / r.
    
        Args:
            request: Centripetal force request
    
        Returns:
            Centripetal force and acceleration
        """
        m = request.mass
        v = request.velocity
        r = request.radius
    
        a_c = (v * v) / r
        F_c = m * a_c
    
        return CentripetalForceResponse(centripetal_force=F_c, centripetal_acceleration=a_c)
  • Pydantic request model for centripetal force calculation with mass (>0), velocity (>=0), and radius (>0) fields.
    class CentripetalForceRequest(BaseModel):
        """Request for centripetal force calculation."""
    
        mass: float = Field(..., description="Mass in kg", gt=0.0)
        velocity: float = Field(..., description="Velocity magnitude in m/s", ge=0.0)
        radius: float = Field(..., description="Radius of circular path in meters", gt=0.0)
  • Pydantic response model containing centripetal_force (N) and centripetal_acceleration (m/s²).
    class CentripetalForceResponse(BaseModel):
        """Response for centripetal force calculation."""
    
        centripetal_force: float = Field(..., description="Centripetal force in Newtons")
        centripetal_acceleration: float = Field(..., description="Centripetal acceleration in m/s²")
  • MCP @tool-decorated async wrapper that registers calculate_centripetal_force as an MCP tool. Accepts mass, velocity, radius as float parameters, delegates to the core calculation function, and returns the result as a dict.
    @tool  # type: ignore[arg-type]
    async def calculate_centripetal_force(
        mass: float,
        velocity: float,
        radius: float,
    ) -> dict:
        """Calculate centripetal force: F_c = m v² / r.
    
        Force required to keep an object moving in a circle.
        Always points toward the center of the circular path.
    
        Args:
            mass: Mass in kg
            velocity: Speed (velocity magnitude) in m/s
            radius: Radius of circular path in meters
    
        Returns:
            Dict containing:
                - centripetal_force: F_c in Newtons
                - centripetal_acceleration: a_c in m/s²
    
        Tips for LLMs:
            - Not a new force - it's the net inward force (tension, friction, gravity)
            - Faster speed → much more force needed (v² relationship)
            - Tighter turn → more force needed
            - Use for: car turns, satellite orbits, centrifuges
    
        Example - Car turning:
            result = await calculate_centripetal_force(
                mass=1500,  # kg
                velocity=20,  # m/s (72 km/h)
                radius=50  # meter turn radius
            )
            # F_c = 12000 N (provided by friction between tires and road)
        """
        from ..circular_motion import CentripetalForceRequest, calculate_centripetal_force as calc_fc
    
        request = CentripetalForceRequest(
            mass=mass,
            velocity=velocity,
            radius=radius,
        )
        response = calc_fc(request)
        return response.model_dump()
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses that it returns a dict with centripetal force and acceleration, explains physics behavior (v² relationship, direction). Lacks explicit statement that it's a pure calculation without side effects.

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

Conciseness5/5

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

Well-structured with formula, explanation, args, returns, tips, and example. Every sentence adds value, front-loaded with essential information.

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

Completeness5/5

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

Given no output schema, the description explains the return dict structure. Covers key physics context, examples, and tips. No missing elements for a calculation tool.

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?

Schema description coverage is 0%, but the description defines each parameter's units and meaning (mass in kg, velocity in m/s, radius in meters). Provides an example with real values, compensating fully for schema's lack of description.

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 the purpose: calculate centripetal force using the formula F_c = m v² / r. It distinguishes from sibling tools like calculate_force and other specific forces by focusing on circular motion.

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

Usage Guidelines4/5

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

Provides tips on when to use (car turns, satellite orbits, centrifuges) and explains it's the net inward force, not a new force. Could be improved by explicitly stating when not to use, but context is clear.

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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