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Physics MCP Server

by IBM

check_torque_balance

Verify rotational equilibrium by checking if net torque equals zero for torque vectors. Essential for solving lever problems and analyzing rotational balance in physics.

Instructions

Check if torques are in equilibrium: Στ = 0.

Verifies whether a system of torques is balanced (net torque = 0).
Essential for rotational equilibrium and lever problems.

Args:
    torques: List of torque vectors [[x,y,z], ...] in N⋅m (or JSON string)
    tolerance: Tolerance for equilibrium check (fraction, default 0.01)

Returns:
    Dict containing:
        - net_torque: Net torque vector [x, y, z] in N⋅m
        - net_torque_magnitude: Net torque magnitude in N⋅m
        - is_balanced: Whether torques are in equilibrium
        - individual_magnitudes: Magnitude of each torque

Example - Seesaw balance:
    result = await check_torque_balance(
        torques=[[0, 0, 100], [0, 0, -100]],
        tolerance=0.01
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
torquesYes
toleranceNo
Behavior3/5

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 explains what the tool does (checks torque equilibrium with tolerance) and describes the return format, but doesn't mention error conditions, performance characteristics, or limitations like input size constraints. It adequately describes the core behavior but lacks comprehensive operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is well-structured and front-loaded with the core purpose, followed by parameter details, return values, and a practical example. Every sentence adds value without redundancy, and the information is organized logically for quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, physics calculation), no annotations, and no output schema, the description does an excellent job explaining parameters and return values. However, it could benefit from mentioning potential edge cases or error conditions to be fully complete for agent invocation.

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 explaining both parameters thoroughly: 'torques' as 'List of torque vectors [[x,y,z], ...] in N⋅m (or JSON string)' and 'tolerance' as 'Tolerance for equilibrium check (fraction, default 0.01)'. The example further clarifies parameter usage with concrete values.

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 ('check', 'verifies') and resource ('system of torques'), distinguishing it from siblings like check_equilibrium or check_force_balance by focusing on rotational equilibrium. It explicitly mentions applications like lever problems, providing clear differentiation.

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 the tool ('essential for rotational equilibrium and lever problems'), but doesn't explicitly state when not to use it or name specific alternatives among siblings. The example helps illustrate usage but doesn't provide exclusion criteria.

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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