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IBM

Physics MCP Server

by IBM

calculate_acceleration_from_position

Calculate acceleration from position data using numerical differentiation. This tool analyzes recorded position and time values to determine velocity and acceleration vectors for motion analysis.

Instructions

Calculate acceleration by numerical differentiation of position data.

Uses central differences for numerical differentiation:
v[i] ≈ (r[i+1] - r[i-1]) / (2Δt)
a[i] ≈ (v[i+1] - v[i-1]) / (2Δt)

Args:
    times: Time values in seconds (or JSON string)
    positions: Position vectors [[x,y,z], ...] in meters (or JSON string)

Returns:
    Dict containing:
        - velocities: Velocity vectors [[x,y,z], ...] in m/s
        - accelerations: Acceleration vectors [[x,y,z], ...] in m/s²
        - average_velocity: Average velocity [x,y,z] in m/s
        - average_acceleration: Average acceleration [x,y,z] in m/s²

Example - Analyze recorded position data:
    result = await calculate_acceleration_from_position(
        times=[0, 1, 2, 3],
        positions=[[0,0,0], [5,0,0], [10,0,0], [15,0,0]]
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timesYes
positionsYes
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 computational method (central differences algorithm), input requirements (time and position data), and output structure (dictionary with velocities, accelerations, and averages). It doesn't mention error handling, performance characteristics, or data validation, but provides substantial operational context.

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 and appropriately sized. It begins with the core purpose, explains the algorithm, details parameters and returns, and provides a concrete example. While comprehensive, every sentence adds value, though the mathematical formulas could be slightly condensed for pure conciseness.

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 computational complexity, no annotations, and no output schema, the description provides strong completeness. It covers purpose, algorithm, parameters, return values, and includes an example. The main gap is lack of explicit error conditions or edge cases (e.g., minimum data points needed), but it's largely sufficient for effective use.

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 (schema only indicates string types), the description fully compensates by providing rich parameter semantics. It explains that 'times' are time values in seconds (or JSON string) and 'positions' are position vectors in meters (or JSON string), including the expected data format [[x,y,z], ...]. This adds crucial meaning 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: 'Calculate acceleration by numerical differentiation of position data.' It specifies the exact mathematical method (central differences) and distinguishes itself from sibling tools like 'calculate_instantaneous_velocity' by focusing on acceleration derivation from position data.

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 this tool: analyzing recorded position data to compute acceleration. It includes an example demonstrating typical usage. However, it doesn't explicitly state when not to use it or mention alternatives among the many sibling physics calculation tools.

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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