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IBM

Physics MCP Server

by IBM

calculate_instantaneous_velocity

Calculate instantaneous velocity vector and speed at a specific time from position data and time values, using interpolation or numerical differentiation.

Instructions

Calculate instantaneous velocity at a specific time.

Uses interpolation if target_time is between data points,
otherwise uses numerical differentiation.

Args:
    positions: Position vectors [[x,y,z], ...] in meters (or JSON string)
    times: Time values in seconds (or JSON string)
    target_time: Time at which to calculate velocity in seconds

Returns:
    Dict containing:
        - velocity: Velocity vector [x,y,z] in m/s
        - speed: Speed magnitude in m/s
        - interpolated: Whether interpolation was used
        - time: Target time (echo)

Example:
    result = await calculate_instantaneous_velocity(
        positions=[[0,0,0], [3,4,0], [6,8,0]],
        times=[0, 1, 2],
        target_time=1.0
    )
    # speed = 5 m/s

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
positionsYes
timesYes
target_timeYes
Behavior4/5

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

The description discloses the algorithm (interpolation/differentiation) and the return structure, including fields like 'interpolated'. Despite no annotations, it is transparent about key behaviors. It lacks details on error handling or assumptions.

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 args, returns, and an example. It is concise yet informative, though the example adds length.

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 no output schema and sparse input schema, the description provides complete context: purpose, method, all parameters, return values, and a concrete example. It leaves no major gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description adds significant meaning: it explains positions as vectors in meters or JSON string, times as seconds, and target_time as time. This compensates well for the sparse 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 explicitly states 'Calculate instantaneous velocity at a specific time' and distinguishes it from siblings like calculate_average_speed by detailing the method (interpolation vs differentiation).

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 explains when interpolation or numerical differentiation is used, providing clear context. However, it does not explicitly state when to use this tool over alternatives or exclude cases where it should not be used.

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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