Skip to main content
Glama

flexsim_list_tree

List FlexSim model tree subnodes as JSON by path, depth, and node limit to inspect simulation hierarchies in window or Python backend.

Instructions

List tree subnodes as JSON (window or Python backend via run_script).

Usa run_script (dual, como flexsim_get_node_value), NO evaluate_node (solo-Python),
asi funciona en ventana. Dado path lista subnodos directos (nombre + clase via
node() + subnodes loop + getname(classobject())) hasta depth con limite max_nodes;
para depth>1 itera niveles con un script por nodo/padre desde Python. Devuelve
JSON {path, nodes: [{name, class, path}]}. path se valida contra inyeccion (solo
alfanumericos, /, _, -, >, :, espacios; rechaza ".." y ";").

Args:
    path: tree path (default "Model").
    depth: 0-3 levels (default 1; 0 returns no children).
    max_nodes: cap for returned nodes (default 100).

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

C2.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the return shape, the depth>1 iteration behavior, and the injection-validation rules on path. However, it never states the operation is read-only/non-mutating, nor any error or permission behavior, and is cluttered with implementation internals rather than agent-relevant traits.

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

Conciseness2/5

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

The text is bilingual and mixes agent-facing purpose with implementer internals (run_script, node(), classobject(), subnodes loop). The key purpose is front-loaded in one line, but the body is bloated and hard to parse, with no clean separation of usage, behavior, and arguments.

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?

An output schema exists, so return-value explanation is not strictly required, and the description still provides it. The Args section covers all three parameters, giving an agent enough to call the tool. But the muddled bilingual phrasing and implementation noise leave the definition only adequate, not complete.

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%, so the description must compensate. The Args block does add some meaning beyond the schema: 'depth: 0 returns no children' and 'max_nodes: cap for returned nodes' clarify semantics the schema does not. But path is only described tautologically as 'tree path', and the rest largely restates defaults/ranges already present in the 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 states a specific verb+resource: 'List tree subnodes as JSON'. That is clear enough for an agent to know what the tool does. It stops short of a crisp sibling differentiation, since the routing hints (run_script vs evaluate_node) concern internal mechanics rather than why an agent would pick this over flexsim_inspect_model or flexsim_get_node_value.

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

Usage Guidelines2/5

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

The description tells the implementer which backend to use ('Usa run_script ... NO evaluate_node'), but that is not agent-facing when-to-use guidance. There is no statement of when to call flexsim_list_tree versus flexsim_inspect_model, flexsim_get_node_value, or other read tools in the family.

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