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IBM

Physics MCP Server

by IBM

check_momentum_conservation

Verify momentum conservation in collisions by comparing initial and final momentum vectors within a specified tolerance to validate physics calculations.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by explaining what it checks (momentum conservation in isolated systems), what it returns (detailed dict with specific metrics), and includes practical tips about vector nature and tolerance handling. It doesn't mention rate limits or authentication needs, but provides substantial behavioral context.

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 every sentence adds value. It could be slightly more concise in the tips section, but overall it's efficiently organized and front-loaded with the core 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?

Given the tool's complexity (physics verification with vector calculations), no annotations, and no output schema, the description provides complete context. It explains the tool's purpose, parameters, return values, usage scenarios, and includes a practical example, making it fully self-contained for an AI agent.

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 providing detailed parameter documentation. It explains each parameter's purpose, format ('[x, y, z] in kg⋅m/s (or JSON string)'), units, and default values, adding significant meaning 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's purpose with specific verbs ('verify', 'checks') and resources ('conservation of momentum', 'collision or interaction'), distinguishing it from siblings like 'check_energy_conservation' or 'check_collision' by focusing specifically 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 Guidelines5/5

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

The description provides explicit guidance on when to use this tool ('to validate collision calculations'), when not to use it ('external forces (friction, etc.) can change total momentum'), and includes tips that help differentiate it from alternatives like 'check_collision' or 'calculate_elastic_collision'.

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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