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IBM

Physics MCP Server

by IBM

calculate_force

Calculate force vectors from mass and acceleration using Newton's Second Law (F=ma) for dynamics and engineering applications.

Instructions

Calculate force from mass and acceleration using Newton's Second Law (F = ma).

Computes the force vector required to produce a given acceleration on a mass.
Fundamental for dynamics, engineering, and understanding motion.

Args:
    mass: Mass in kilograms (must be positive)
    acceleration_x: X component of acceleration in m/s²
    acceleration_y: Y component of acceleration in m/s²
    acceleration_z: Z component of acceleration in m/s²

Returns:
    ForceCalculationResponse containing:
        - force: Force vector [x, y, z] in Newtons
        - magnitude: Force magnitude in Newtons

Tips for LLMs:
    - 1 Newton = force to accelerate 1 kg at 1 m/s²
    - On Earth, weight force = mass × 9.81 N (vertical)
    - Use magnitude to compare total force regardless of direction
    - Common accelerations: car braking ~10 m/s², elevator ~2 m/s²

Example:
    # Force to accelerate a 1500kg car at 3 m/s² forward
    result = await calculate_force(
        mass=1500.0,
        acceleration_x=3.0,
        acceleration_y=0.0,
        acceleration_z=0.0
    )
    print(f"Required force: {result.magnitude:.0f} N")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
massYes
acceleration_xYes
acceleration_yYes
acceleration_zYes
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 does this well by explaining the calculation method (Newton's Second Law), providing practical examples, and including educational context about units and real-world applications. The only gap is it doesn't mention error handling or computational constraints.

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 front-loads the core functionality. While comprehensive, some tips could be more concise, but every section adds value for an AI agent understanding and using the tool correctly.

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 (physics calculation with 4 parameters), no annotations, and no output schema, the description provides excellent completeness. It explains the calculation method, documents all parameters, describes the return structure, provides practical examples, and includes educational context - everything needed for an AI agent to use this tool effectively.

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, units (kilograms, m/s²), constraints ('must be positive' for mass), and clarifies the 3D vector nature of acceleration. This 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 clearly states the tool calculates force from mass and acceleration using Newton's Second Law (F=ma), providing a specific verb ('calculate') and resource ('force'). It distinguishes from sibling tools like 'calculate_centripetal_force' or 'calculate_drag_force' by specifying it's the fundamental Newtonian force calculation.

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 to use this tool ('fundamental for dynamics, engineering, and understanding motion') and includes helpful tips for LLMs. However, it doesn't explicitly state when NOT to use it or name specific alternative tools from the sibling list for different force calculations.

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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