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IBM

Physics MCP Server

by IBM

calculate_elastic_collision_3d

Calculate final velocities after a 3D elastic collision where kinetic energy and momentum are conserved. Use for billiard balls, Newton's cradle, and other idealized systems.

Instructions

Calculate 3D elastic collision (perfect energy conservation).

Special case of collision where no kinetic energy is lost (e = 1.0).
Both momentum and energy are conserved.

Args:
    mass1: Mass of object 1 in kg
    velocity1: Velocity of object 1 [x, y, z] in m/s (or JSON string)
    mass2: Mass of object 2 in kg
    velocity2: Velocity of object 2 [x, y, z] in m/s (or JSON string)

Returns:
    Dict containing:
        - final_velocity1: Final velocity [x, y, z] in m/s
        - final_velocity2: Final velocity [x, y, z] in m/s
        - initial_momentum: Total momentum [x, y, z]
        - final_momentum: Total momentum [x, y, z]
        - initial_kinetic_energy: Total KE in Joules
        - final_kinetic_energy: Total KE in Joules

Tips for LLMs:
    - Ideal approximation for billiard balls, Newton's cradle
    - Both momentum and energy conserved
    - Equal masses + head-on → velocities exchange
    - Use for educational examples, idealized systems

Example - Pool balls:
    result = await calculate_elastic_collision_3d(
        mass1=0.17,  # kg (pool ball)
        velocity1=[2, 0, 0],  # 2 m/s
        mass2=0.17,  # kg
        velocity2=[0, 0, 0]  # stationary
    )
    # Result: ball 1 stops, ball 2 moves at 2 m/s

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mass1Yes
velocity1Yes
mass2Yes
velocity2Yes
Behavior4/5

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

With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it's a calculation tool (not a simulation), specifies the physics model (elastic collision with e=1.0), and explains conservation principles. It could mention computational limits or precision but covers the essential behavior well.

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 well-structured with clear sections (purpose, args, returns, tips, example) and front-loaded key information. It's appropriately sized for a 4-parameter calculation tool, though the example section is somewhat lengthy but adds practical value.

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 the tool's complexity (physics calculation with 4 parameters) and no annotations or output schema, the description provides excellent completeness. It covers purpose, parameters, return values, usage context, and includes a practical example. The return value documentation effectively substitutes for a missing output schema.

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?

With 0% schema description coverage, the description fully compensates by documenting all 4 parameters with clear semantics: mass in kg, velocities as 3D vectors in m/s (or JSON strings). It provides units, format details, and example values that go beyond what the bare schema provides.

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 tool calculates 3D elastic collisions with perfect energy conservation, distinguishing it from sibling tools like 'calculate_elastic_collision' (2D) and 'calculate_inelastic_collision_3d' (energy loss). It specifies the physics context (momentum and energy conservation) and the special case (e = 1.0).

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?

The description provides clear context for when to use this tool (ideal approximation for billiard balls, Newton's cradle, educational examples, idealized systems). However, it doesn't explicitly state when NOT to use it or mention alternatives like the inelastic collision tool, though the physics context implies it's for perfect conservation scenarios.

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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