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IBM

Physics MCP Server

by IBM

calculate_average_speed

Calculate average speed from position and time data to analyze motion along a path, returning speed, distance, time, and displacement metrics.

Instructions

Calculate average speed along a path.

Average speed = total distance / total time
(Distance is path length, not displacement)

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

Returns:
    Dict containing:
        - average_speed: Average speed in m/s
        - total_distance: Total path length in meters
        - total_time: Total elapsed time in seconds
        - displacement_magnitude: Straight-line displacement in meters
        - displacement: Displacement vector [x,y,z] in meters

Example - Car on winding road:
    result = await calculate_average_speed(
        positions=[[0,0,0], [10,5,0], [20,10,0], [15,20,0]],
        times=[0, 10, 20, 30]
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
positionsYes
timesYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It effectively discloses behavioral traits: it's a calculation tool (non-destructive, read-only implied), specifies input formats ('Position vectors [[x,y,z], ...] in meters (or JSON string)'), output structure ('Dict containing...'), and includes an example. However, it doesn't mention error handling or rate limits, leaving minor gaps.

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 appropriately sized and front-loaded, starting with the core purpose and formula, followed by parameter details, return values, and an example. Each sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 (calculation tool with 2 parameters), no annotations, 0% schema coverage, and no output schema, the description is complete enough. It covers purpose, parameters, return values, and includes an example, providing all necessary context for an AI agent to select and invoke the tool correctly without relying on structured fields.

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 schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the input schema by explaining both parameters: 'positions' as 'Position vectors [[x,y,z], ...] in meters (or JSON string)' and 'times' as 'Time values in seconds (or JSON string)', including units and formats, which the schema lacks entirely.

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 a specific verb ('calculate') and resource ('average speed along a path'), including the formula 'average speed = total distance / total time' and clarifying that distance is 'path length, not displacement'. This distinguishes it from sibling tools like 'calculate_instantaneous_velocity' or 'calculate_displacement' (implied).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for calculating average speed from position and time data, with an example scenario ('Car on winding road'), but does not explicitly state when to use this tool versus alternatives like 'calculate_instantaneous_velocity' or 'fit_trajectory'. It provides context but lacks explicit guidance on exclusions or comparisons.

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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