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FelipeDiazAimar

FlexSim MCP Server

by FelipeDiazAimar
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flexsim_experiment_plan

Plan read-only FlexSim Experimenter scenarios: detect Experimenter, list variable settings, and return a ready-to-run configuration with seeds and run parameters, without launching jobs.

Instructions

Plan read-only de escenarios del Experimenter (pre-vuelo, sin lanzar).

SOLO LECTURA (nunca escribe): detecta el Experimenter via Model.find(name)
y/o scan por clase conteniendo "experiment" (patron de la vecina
flexsim_experimenter_status) y lista escenarios como subnodos. Por cada
escenario lee hasta 20 subnodos como nombre=valor texto
(`sc.subnodes[i].name + "=" + sc.subnodes[i].value`, como `>variables`
en _chart_inspect_map) via run_script dual (ventana o Python). Solo las
entradas con forma clara variable=numero van a params; las demas van a
unreadable con sus nombres. NUNCA crea, escribe ni lanza jobs.

Args:
    name: nombre del Experimenter (default Experimenter1).
    default_seeds: semillas para flexsim_run_experiments (default [101,202,303]).
    default_run_to_time: tiempo simulado para flexsim_run_experiments (default 600).
    default_run_speed: Run Speed slider para flexsim_run_experiments (default 1000).

Returns JSON {experimenter, exists, scenarios:[{name, params, unreadable,
    source:"variables"}], seeds, run_to_time, run_speed, ready, usage}
    (+ hint cuando no existe: crearlo a mano en Toolbox).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior5/5

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

No annotations, so description carries full burden. Thoroughly discloses: detection method, reading up to 20 subnodes, parsing logic (variable=number into params, others into unreadable), dual run_script, and return structure with hint when missing. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded purpose is clear, but the description is verbose with implementation details (Model.find, subnodes[i].name expressions, run_script dual) that may exceed what an agent needs, slightly reducing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema exists, so return values needn't be detailed, but the description omits the required 'params' wrapper, creating a completeness gap for correct invocation. Otherwise behavior and usage are well-covered.

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

Parameters2/5

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

Schema coverage 0% and the top-level parameter is a nested 'params' object, but description lists name, default_seeds, etc. as direct args without mentioning the wrapper. This structural mismatch could lead to incorrect invocation; meaning of each param is given but not the required nesting.

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?

States a specific verb and resource: 'Plan read-only de escenarios del Experimenter (pre-vuelo, sin lanzar).' Clearly distinguishes from siblings by referencing flexsim_experimenter_status and providing defaults for flexsim_run_experiments, so an agent can tell it apart.

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?

Provides clear context: pre-flight planning, read-only, never launches jobs. Implies when to use (before running experiments) but does not explicitly contrast alternatives like flexsim_experimenter_status or flexsim_run_experiments beyond mentioning them.

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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