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list_queues

List batch queues on a Tapis execution system to view queue names, max nodes, and max minutes for your computational workflows.

Instructions

List batch queues available on a Tapis execution system.

Args: system_id (str): The ID of the execution system (e.g., "stampede3"). output (str, optional): "df" for DataFrame (default), "raw" for Tapis objects.

Returns: DataFrame or List: Queues with name, maxNodes, maxMinutes, etc.

Example: >>> ds.systems.queues("stampede3")

[schema introspected from dapi 0.6.1 (systems.queues); this tool is generated, not maintained by hand]

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "system_id": "<system_id>"
    },
    "name": "list_queues"
  }
}

Input Schema

Name	Required	Description	Default
`output`	No		df
`system_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 12, 2026

TDQS

A4.3/5.0

Behavior4/5

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

No annotations are present, so the description carries the transparency burden. It discloses return formats ('df' vs 'raw'), example output fields (name, maxNodes), and notes that the tool is generated, which is useful behavioral context beyond the schema.

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?

The description is organized into Args, Returns, and Example sections, with no filler. The generated-tool note adds useful context and is clearly marked, though it adds a slight preamble.

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 simple list operation with no output schema, the description provides sufficient information about inputs, outputs, and invocation. It lacks deeper error/auth details, but those are less critical for this type of tool.

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%, but the description explicitly defines both parameters, including a concrete example value for system_id and the optional 'output' choices. This fully compensates for the missing schema descriptions.

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 begins with a specific verb and resource: 'List batch queues available on a Tapis execution system.' This clearly distinguishes it from sibling tools like list_systems and list_app_templates.

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?

The description explains the required system_id and provides a usage example, making it clear when to call this tool. It does not explicitly mention alternatives, but the scope is evident from the resource it targets.

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