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AIops-tools

identity-aiops

undo_list

List pending undo tokens for applied tool actions, showing the inverse operation and verification status so you can decide whether to apply the rollback.

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 behavioral burden. It details the return shape, explains that truncated is measured (one extra row fetched), and warns that effectVerified=false means the change is only probable, not confirmed. This goes beyond a typical list tool description.

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 lengthy but every sentence adds value. It is front-loaded with the core purpose and uses structured paragraphs for nuances like truncation and effectVerified, with an Args section for parameters.

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?

There is no output schema, so the description must explain return values, which it does including the JSON structure and the meaning of truncated and effectVerified. It also covers limit caps and target's irrelevance, making it complete for the tool's complexity.

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?

Both parameters are fully explained: limit is described as max rows with default 50 and cap 500, and target is noted as unused but kept for CLI uniformity. This compensates for the schema's 0% description coverage.

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 '[READ] List recorded, not-yet-applied undo tokens (most recent first).' This clearly identifies the verb (list), resource (undo tokens), and scope (not-yet-applied), distinguishing it from the sibling undo_apply which applies them.

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 directs the reader to use the undoId with undo_apply, providing a clear follow-up action. However, it doesn't explicitly state when not to use this tool or compare it to other list tools, so it falls short of a fully explicit usage policy.

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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