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KevinInoCol

coppeliasim-mcp

by KevinInoCol

paso_simulacion

Advance the simulation in exact, fixed steps to make readings repeatable and return the simulated time. This gives identical measurements across runs by preventing free-running timing drift.

Instructions

Avanza la simulación un número exacto de pasos y devuelve el tiempo.

Para qué sirve: con la simulación en marcha libre no se puede medir nada de forma repetible, porque entre una lectura y la siguiente pasa el tiempo que quiera el reloj. Avanzando por pasos, cada lectura cae siempre en el mismo instante simulado, y dos ejecuciones dan el mismo número.

Deja la simulación en modo por pasos: a partir de aquí solo avanza cuando se lo pidas. detener_simulacion la devuelve a marcha libre, y conviene llamarla al terminar: una simulación esperando un paso que nadie manda parece congelada.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pasosNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.4.0

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses the side effect of leaving the simulation in step mode, states that the simulation will only advance when requested afterward, and warns that a waiting step-mode simulation may appear frozen.

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 and front-loaded: the core action and return value come first, followed by a concise rationale, then side-effect and cleanup guidance. Every sentence serves a distinct purpose, and there is no redundant repetition of schema details.

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?

For a one-parameter tool with no annotations and an existing output schema, the description is complete. It explains what the tool does, why to use it, how it changes simulation mode, and what to do afterward. The return format is covered by the output schema, so its absence in the description is acceptable.

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?

The input schema provides only the parameter name 'pasos', type integer, and default 1, with no property description. The description adds meaningful semantics by explaining that 'pasos' is the exact number of simulation steps to advance. It does not discuss constraints like positivity, but for a single simple parameter this is a minor gap.

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 opens with a specific action and resource: 'Avanza la simulación un número exacto de pasos y devuelve el tiempo.' It clearly communicates that this tool advances a simulation by a fixed number of steps and returns the time, and it is easy to distinguish from related simulation-control tools.

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

Usage Guidelines5/5

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

The description explicitly explains when to use the tool: when repeatable, deterministic measurements are needed because free-running simulation timing is not reproducible. It also names 'detener_simulacion' as the cleanup call and explains the consequence of not calling it, which is strong practical guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.