FlexSim MCP Server

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flexsim_library_list

Lists FlexSim library class names in a group as JSON, with optional substring filtering. Use it to discover available classes for building or configuring models.

Instructions

List library class names in a group as JSON (window or Python backend). # LIB-people

Solo lectura (dual via run_script, como flexsim_dashboard_list): usa el
patron VERIFICADO de AGENTE_FLEXSIM.md sec 4:
treenode g = library().find("?group"); loop g.subnodes[i].name.

Vecinas leidas antes de escribir: flexsim_person_create (una sola clase
libre del grupo people) y flexsim_dashboard_list (filtra por clase en
Python con subcadena).

Grupos conocidos (AGENTE_FLEXSIM.md sec 4): fixedresources, taskexecuters,
conveyor, warehousing, people, travelnetworks, statistics, processflow,
fluid, AGV, astar, agent, Submodels.

Args:
    group: grupo de la biblioteca (default "people"). Validado con
        ^[A-Za-z0-9_]+$ (sin inyeccion: se cita con fs_string).
    pattern: subcadena opcional para filtrar clases (default "" = sin
        filtro). Subcadena Python `in`, sin regex inyectable (nunca va al
        FlexScript).

Returns JSON {group, count, classes:[...filtradas...]}. Si el grupo no
existe devuelve error claro con la lista de grupos conocidos.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 5, 2026

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 disclose useful behavior: it is read-only, works on a dual backend, and returns a clear error listing known groups when the group is invalid. It does not mention permissions or latency, but the safety profile and error path are covered.

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?

The purpose is front-loaded in the first line, but the body is bloated with implementation notes (a FlexScript snippet, 'Vecinas leidas antes de escribir', a full known-groups list) that serve the author more than an agent choosing a tool. Content is useful but not tight.

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?

For a two-parameter read tool with an output schema present, the description need not explain return values, and it still covers parameters, error behavior, and valid group names. An agent has enough to call it correctly, though permissions and backend selection could be clearer.

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?

Schema description coverage is 0%, so the description must compensate, and it does: it explains the group default ('people'), its regex validation and injection protection, and the pattern substring semantics (optional, Python `in`, not a regex, never sent to FlexScript). This meaningfully exceeds the bare schema.

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?

The opening sentence states a specific verb and resource: 'List library class names in a group as JSON', so an agent knows exactly what it returns. It also references siblings (flexsim_dashboard_list, flexsim_person_create) to situate itself, though the distinction is by analogy rather than by explicit contrast.

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?

It signals read-only use and cites a documented pattern ('Solo lectura (dual via run_script...)' referencing AGENTE_FLEXSIM.md sec 4), but never states explicitly when to reach for this tool versus the many other list/introspection siblings. Usage is implied rather than directed.

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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