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IBM

Physics MCP Server

by IBM

calculate_angular_momentum

Calculate angular momentum vectors and magnitudes for rotating systems using moment of inertia and angular velocity components. This physics tool helps analyze rotational motion and conservation principles.

Instructions

Calculate angular momentum: L = I × ω.

Angular momentum is the rotational equivalent of linear momentum.
It's conserved in the absence of external torques (like ice skater spinning).

Args:
    moment_of_inertia: Moment of inertia in kg⋅m²
    angular_velocity_x: X component of angular velocity in rad/s
    angular_velocity_y: Y component of angular velocity in rad/s
    angular_velocity_z: Z component of angular velocity in rad/s

Returns:
    Dict containing:
        - angular_momentum: L vector [x, y, z] in kg⋅m²/s
        - magnitude: L magnitude in kg⋅m²/s

Tips for LLMs:
    - Angular momentum is conserved when no external torques act
    - Ice skater pulls arms in → I decreases → ω increases (L constant)
    - Gyroscopes resist changes in angular momentum direction

Example - Spinning figure skater:
    # Arms extended: I = 3.0 kg⋅m², ω = 5 rad/s
    result = await calculate_angular_momentum(
        moment_of_inertia=3.0,
        angular_velocity_x=0.0,
        angular_velocity_y=5.0,
        angular_velocity_z=0.0
    )
    # L = 15 kg⋅m²/s (conserved when arms pulled in)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
moment_of_inertiaYes
angular_velocity_xYes
angular_velocity_yYes
angular_velocity_zYes
Behavior4/5

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

With no annotations provided, the description carries full burden and does an excellent job. It explains the calculation is deterministic (formula-based), provides conservation principles, gives practical physics examples (ice skater, gyroscopes), and includes a complete example with expected output. The only minor gap is not explicitly stating this is a pure calculation with no 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.

Conciseness4/5

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

The description is well-structured with formula, explanation, parameter details, return values, tips, and example. While comprehensive, some sections like the conservation explanation could be more concise. Every sentence adds value, but the ice skater example appears twice (in tips and example section).

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 calculation tool with no annotations and no output schema, the description provides exceptional completeness. It covers purpose, parameters, return format, physics context, practical examples, and usage guidance. The agent has everything needed to correctly invoke this 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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. Each parameter gets clear units (kg⋅m², rad/s), physical meaning, and the example shows proper usage. The description adds substantial value beyond the bare schema.

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 explicitly states the tool calculates angular momentum using the formula L = I × ω, providing a specific verb (calculate) and resource (angular momentum). It clearly distinguishes from sibling tools like calculate_momentum (linear momentum) and calculate_torque by focusing specifically on rotational dynamics.

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 about when angular momentum calculations are relevant (rotational systems, conservation scenarios), and the 'Tips for LLMs' section gives practical examples. However, it doesn't explicitly state when to use this tool versus alternatives like calculate_rotational_kinetic_energy or check_angular_momentum_conservation.

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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