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IBM

Physics MCP Server

by IBM

check_energy_conservation

Verify conservation of mechanical energy in physics processes by comparing initial and final kinetic/potential energy values. Validates simulation accuracy and accounts for expected energy losses from friction or damping.

Instructions

Verify conservation of energy in a physics process.

Checks whether total mechanical energy is conserved (or correctly dissipated).
Useful for validating simulation results and understanding energy transfer.

Args:
    initial_kinetic_energy: Initial KE in Joules
    final_kinetic_energy: Final KE in Joules
    initial_potential_energy: Initial PE in Joules
    final_potential_energy: Final PE in Joules
    expected_energy_loss: Expected energy loss (from friction, etc.) in Joules
    tolerance: Tolerance for conservation check (fraction, default 0.01 = 1%)

Returns:
    Dict containing:
        - initial_total_energy: Initial total energy in Joules
        - final_total_energy: Final total energy in Joules
        - energy_difference: Energy difference in Joules
        - energy_difference_percent: % difference
        - is_conserved: Whether energy is conserved within tolerance
        - expected_loss: Expected energy loss in Joules
        - actual_loss: Actual energy loss in Joules

Tips for LLMs:
    - In isolated systems, total energy is conserved
    - With friction/damping, expect energy loss
    - Small numerical errors are normal in simulations
    - Use to validate simulation accuracy

Example - Bouncing ball with energy loss:
    result = await check_energy_conservation(
        initial_kinetic_energy=0,
        final_kinetic_energy=0,
        initial_potential_energy=10,  # J (at 1m height)
        final_potential_energy=6.4,  # J (bounced to 0.64m)
        expected_energy_loss=3.6,  # 36% loss (e=0.8)
        tolerance=0.01
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
initial_kinetic_energyYes
final_kinetic_energyYes
initial_potential_energyYes
final_potential_energyYes
expected_energy_lossNo
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: it performs a verification check with tolerance, returns a detailed dict with metrics like energy difference and conservation status, and includes practical tips for LLMs (e.g., 'Small numerical errors are normal in simulations'). It covers key aspects like input handling (defaults for expected_energy_loss and tolerance) and output structure, though it could mention error handling or performance considerations.

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 sections for purpose, usage, args, returns, tips, and an example. It is appropriately sized for a complex tool with 6 parameters and no output schema. However, it could be slightly more concise by integrating some tips into the usage section, and the example is detailed but necessary for clarity.

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 complexity (6 parameters, no annotations, no output schema), the description is highly complete. It explains the tool's purpose, usage, all parameters with semantics, return values in detail, and includes practical tips and an example. This compensates fully for the lack of structured data, ensuring the agent can correctly invoke and interpret results.

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 description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all 6 parameters: each is clearly defined with units (Joules for energy, fraction for tolerance) and purpose (e.g., 'Initial KE in Joules'). It explains defaults (tolerance default 0.01) and required vs. optional parameters, adding significant 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 clearly states the tool's purpose: 'Verify conservation of energy in a physics process' and 'Checks whether total mechanical energy is conserved (or correctly dissipated).' It specifies the verb 'verify/check' and resource 'energy conservation,' distinguishing it from sibling tools like 'track_energy_dissipation' or 'calculate_kinetic_energy' by focusing on validation rather than calculation or tracking.

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: 'Useful for validating simulation results and understanding energy transfer.' It includes tips like 'In isolated systems, total energy is conserved' and 'With friction/damping, expect energy loss,' which guide usage. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as 'check_momentum_conservation' for different conservation laws.

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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