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Physics MCP Server

by IBM

check_momentum_conservation

Verify total momentum conservation in collisions by comparing initial and final momentum vectors within a given tolerance.

Instructions

Verify conservation of momentum.

Checks whether total momentum is conserved in a collision or interaction.
Momentum should be conserved in isolated systems (no external forces).

Args:
    initial_momentum: Initial total momentum [x, y, z] in kg⋅m/s (or JSON string)
    final_momentum: Final total momentum [x, y, z] in kg⋅m/s (or JSON string)
    tolerance: Tolerance for conservation check (fraction, default 0.01 = 1%)

Returns:
    Dict containing:
        - initial_momentum_magnitude: Initial |p| in kg⋅m/s
        - final_momentum_magnitude: Final |p| in kg⋅m/s
        - momentum_difference: Difference [x, y, z]
        - momentum_difference_magnitude: |Δp|
        - momentum_difference_percent: % difference
        - is_conserved: Whether momentum is conserved within tolerance

Tips for LLMs:
    - Momentum is ALWAYS conserved in isolated systems
    - Vector quantity - direction matters
    - Use to validate collision calculations
    - External forces (friction, etc.) can change total momentum

Example - Collision verification:
    result = await check_momentum_conservation(
        initial_momentum=[3000, 0, 0],  # kg⋅m/s
        final_momentum=[2995, 5, 0],  # slightly off
        tolerance=0.01
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
initial_momentumYes
final_momentumYes
toleranceNo
Behavior4/5

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

No annotations provided, so description fully covers behavior: vector nature, direction importance, external forces impact, and example. Could briefly mention that it is a pure computation without side effects, but overall informative.

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?

Well-structured with Args, Returns, Tips, and Example sections. Slightly verbose in tips (e.g., 'Momentum is ALWAYS conserved...') but overall efficient and front-loaded with purpose.

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?

No output schema, but description explains all return fields. Covers physics context, parameter types, tolerance, and example usage. Complete for an LLM to use effectively.

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 has 0% description coverage, but the description provides detailed parameter explanations: initial_momentum and final_momentum as [x,y,z] vectors in kg·m/s, tolerance as fraction. Also fully explains the return dict, exceeding what schema alone offers.

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 it verifies conservation of momentum and distinguishes from siblings like check_energy_conservation and check_angular_momentum_conservation by explicitly focusing on momentum conservation.

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?

Indicates it is used for isolated systems and collision calculations, with tips about external forces. Does not explicitly mention when not to use or alternatives, but context from sibling tools and physics knowledge fills the gap.

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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