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stack_ps

Read-only

List tasks in a Docker stack to see where each runs and why any failed. Get a per-task view across all services to diagnose issues.

Instructions

List the tasks of a stack, parsed from --format '{{json .}}'.

Task-level view across every service in the stack (service_ps covers one service): where each task runs and why it failed. Requires a swarm manager. Raises RuntimeError if the CLI call fails.

args: name - The stack to list tasks for no_trunc - Do not truncate task IDs / errors in the output filters - Filter by attributes, e.g. {"desired-state": "running"}; a list value repeats the filter returns: list - One dict per task (id, name, node, image, desired/current state, error)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
filtersNo
no_truncNo
Behavior5/5

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

Beyond the readOnlyHint and destructiveHint annotations, the description reveals the JSON parsing behavior, error raising ('Raises RuntimeError if the CLI call fails'), and the return format. This meaningfully enriches the agent's understanding of the tool's runtime behavior.

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 a summary, context, error note, and explicit args/returns sections. It is slightly verbose but every sentence adds value, and the structure makes it easy to scan.

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

Completeness5/5

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

Given the tool's complexity (3 params, nested filters, no output schema), the description covers the purpose, usage context, error behavior, parameter semantics, and return structure. No gaps remain for an agent to invoke it correctly.

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

Parameters5/5

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

Although the schema has zero parameter descriptions, the 'args' section explains each parameter (name, no_trunc, filters) and provides an example for filters. 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 opens with 'List the tasks of a stack,' which is a specific verb and resource, and further clarifies the scope by contrasting with service_ps ('service_ps covers one service'). This makes the tool's unique function unmistakable.

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?

It states a prerequisite ('Requires a swarm manager') and provides an explicit alternative for single-service views ('service_ps covers one service'). It does not list exhaustive when-not-to-use conditions, but the context is clear enough for most cases.

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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