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AIops-tools

container-host-aiops

by AIops-tools

undo_list

List recorded undo tokens for container-host operations, including undo IDs and verification status, to identify and apply rollbacks via undo_apply.

Instructions

[READ] List recorded, not-yet-applied undo tokens (most recent first).

Each entry names the original tool, the inverse tool that undo_apply would run, and a human note. Use the undoId with undo_apply.

Returns {"undos": [...], "returned": N, "limit": L, "truncated": bool}. truncated is measured (one extra row is fetched), not guessed from a length coincidence: when it is true there are MORE tokens than shown, so re-run with a higher limit rather than reporting the list as complete.

Each entry carries effectVerified. False means the original write lost its response, so the change it reverses is PROBABLE, not confirmed — check the live state before applying, and do not report the result as a restore of a state that may never have been reached.

Args: limit: Max rows to return (default 50, capped at 500). target: Unused (undo state is host-local); accepted for CLI uniformity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
targetNo
Behavior5/5

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

With no annotations, the description carries full burden and excels. It declares the tool as read-only ('[READ]'), details the truncated behavior with precise explanation ('measured, not guessed'), and explains the effectVerified field's implications for probable vs. confirmed changes.

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

Conciseness5/5

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

The description is well-structured, starting with a clear purpose, then explaining the return format, followed by parameter details. Every sentence is informative with no fluff.

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?

Despite no output schema, the description fully documents the return structure (undos, returned, limit, truncated) and the nuance of effectVerified. All parameters are explained, and the tool's role in the undo workflow is clear.

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?

Schema coverage is 0%, but the description fully compensates. It specifies limit's default (50) and cap (500), and explains target is unused but included for uniformity. This adds significant meaning beyond the bare schema.

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 clearly states '[READ] List recorded, not-yet-applied undo tokens (most recent first).' It specifies the verb (list), resource (undo tokens), and scope (recorded, not-yet-applied), distinguishing it from sibling tools like list_containers or undo_apply.

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 primary use case: listing undo tokens for applying with undo_apply. It also provides guidance on interpreting the truncated field and suggests re-running with a higher limit. However, it does not explicitly state when not to use this tool versus alternatives.

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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