Skip to main content
Glama
IBM

Physics MCP Server

by IBM

check_angular_momentum_conservation

Verify conservation of angular momentum by comparing initial and final values within a specified tolerance to determine if external torques are present.

Instructions

Verify conservation of angular momentum.

Checks whether total angular momentum is conserved. Angular momentum
is conserved when no external torques act on the system.

Args:
    initial_angular_momentum: Initial L [x, y, z] in kg⋅m²/s (or JSON string)
    final_angular_momentum: Final L [x, y, z] in kg⋅m²/s (or JSON string)
    tolerance: Tolerance (fraction, default 0.01 = 1%)

Returns:
    Dict containing:
        - initial_L_magnitude: Initial |L| in kg⋅m²/s
        - final_L_magnitude: Final |L| in kg⋅m²/s
        - L_difference: Difference [x, y, z]
        - L_difference_magnitude: |ΔL|
        - L_difference_percent: % difference
        - is_conserved: Whether L is conserved within tolerance

Tips for LLMs:
    - Conserved when no external torques (isolated rotation)
    - Ice skater spinning: pull arms in → I decreases → ω increases (L constant)
    - Gyroscope: resists changes to L direction
    - Planets orbiting: L conserved → elliptical orbits

Example - Figure skater:
    # Arms extended → Arms pulled in
    result = await check_angular_momentum_conservation(
        initial_angular_momentum=[0, 15, 0],  # kg⋅m²/s
        final_angular_momentum=[0, 15.05, 0],
        tolerance=0.01
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
initial_angular_momentumYes
final_angular_momentumYes
toleranceNo
Behavior5/5

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 comprehensively explains what the tool does: it compares initial and final angular momentum vectors, calculates magnitude differences and percentages, and returns a structured dictionary with specific fields. The example demonstrates exact usage patterns and expected outputs.

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. While comprehensive, the 'Tips for LLMs' section could be slightly more concise, but overall the information density is high with minimal redundancy.

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?

For a tool with no annotations, no output schema, and 0% schema description coverage, the description provides complete context. It explains the tool's purpose, when to use it, all parameter details, return value structure, and includes practical examples. The physics context and sibling tool differentiation make this fully self-contained.

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⋅m²/s (or JSON string)'), units, and default values. The example shows concrete usage with actual values, making parameter expectations completely clear.

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: 'Verify conservation of angular momentum' with a specific verb ('verify') and resource ('angular momentum conservation'). It distinguishes from siblings like 'check_momentum_conservation' (linear momentum) and 'check_energy_conservation' by focusing specifically on angular momentum.

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: 'Angular momentum is conserved when no external torques act on the system.' The 'Tips for LLMs' section further clarifies usage contexts with concrete examples: isolated rotation systems, ice skaters, gyroscopes, and planetary orbits. This gives clear situational guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/IBM/chuk-mcp-physics'

If you have feedback or need assistance with the MCP directory API, please join our Discord server