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IBM

Physics MCP Server

by IBM

calculate_acceleration_from_position

Compute velocity and acceleration vectors from position data using central difference numerical differentiation. Provides average velocity and acceleration.

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?

No annotations exist, so the description carries full responsibility. It details the numerical method (central differences), explains input format flexibility (arrays or JSON strings), and outlines the return structure. However, it does not discuss edge cases like non-uniform time steps, insufficient data points, or error handling.

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 with separate sections for purpose, formulas, arguments, return values, and an example. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 moderate complexity (numerical differentiation), the description covers the core method, input/output details, and an example. It is nearly complete, though it could mention the minimum number of time points required (at least 3 for central differences) and whether uniform time steps are assumed.

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?

The input schema has 0% description coverage, but the description compensates fully by explaining that 'times' are time values in seconds (or JSON string) and 'positions' are position vectors in meters (or JSON string), with a concrete example demonstrating the expected format.

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 acceleration from position data via numerical differentiation using central differences. It specifies the formulas and distinguishes itself from sibling tools like calculate_instantaneous_velocity and calculate_jerk by focusing on acceleration from position history.

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 an example usage for analyzing recorded position data. It implies the tool is for discrete time-series data but does not explicitly exclude other use cases or mention alternative tools for different scenarios.

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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