Skip to main content
Glama
FelipeDiazAimar

FlexSim MCP Server

by FelipeDiazAimar
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

flexsim_get_performance_measure

Retrieve a model-defined performance measure such as utilization or throughput from FlexSim simulation statistics for analysis.

Instructions

Read a performance measure defined in the model (Statistics > Performance Measures).

This is the native way to get statistics out of a model, e.g. utilization or throughput.

Dual backend: with the FlexSim window connected reads the treenode
Model.find("Tools/PerformanceMeasureTables/PerformanceMeasures>variables/measures/<name>")
by script (discovery inline: when that path does not resolve, lists
Tools/PerformanceMeasureTables and returns the available measures as help);
without window uses controller.getPerformanceMeasure. Never creates nor
modifies rows (PerformanceMeasureTables.addRow broke OnReset).

Args:
    name: Performance measure name as defined in the model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it discloses the dual-backend behavior (window vs controller), the inline discovery fallback that lists available measures, and explicitly that it never creates nor modifies rows (citing a prior OnReset breakage). It stops short of stating permissions or read-only guarantees in annotation terms, but the behavioral picture is clear.

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?

Purpose is front-loaded, the Args section is separated, and the backend/path detail earns its place as behavioral transparency. The treenode path is dense but justified by the discovery fallback explanation.

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?

An output schema exists, so return values need not be described, and the description still covers backend routing, the discovery fallback, and mutation safety. The main gap is the absence of any routing to or contrast with flexsim_get_statistics.

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

Parameters3/5

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

Schema description coverage is 0% (the 'name' property has only type/title/minLength), so the description must compensate. It adds 'as defined in the model', hinting the value must reference an existing measure, but provides no format, casing, or lookup guidance beyond that. Minimal added value for a single required param.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Read a performance measure defined in the model', and clarifies it is the native statistics-retrieval path. It does not, however, explicitly differentiate itself from the sibling flexsim_get_statistics, which appears to overlap in purpose.

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?

'This is the native way to get statistics out of a model' implies a usage context but never says when to choose this over flexsim_get_statistics or other read tools. No exclusions or alternatives are named, leaving selection to inference.

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

Deploy Server

Other Tools