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AIops-tools

io.github.AIops-tools/network-aiops

undo_list

List recorded undo tokens that have not yet been applied. Each token includes the undo ID, original tool, inverse tool, and verification status.

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 provided, the description fully discloses behavioral traits: it explains that the truncated flag is measured by fetching one extra row, describes the effectVerified field and its implications for probable vs. confirmed state, and notes that the target parameter is unused for CLI uniformity. This goes beyond minimal requirements.

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 front-loaded with the purpose and well-structured. It covers necessary details without excessive verbosity. Some minor repetition could be trimmed, but it remains efficient and clear.

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 (undo tokens, effectVerified, truncated behavior, parameter quirks), the description is fully complete. It details the return structure, usage with undo_apply, and caveats. No output schema, but the description compensates adequately.

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%, so the description must compensate. It clearly explains limit's default and cap (50, 500), and target's purpose (unused, for uniformity). This adds significant value beyond the schema, although the limit explanation could mention that it's optional.

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 starts with "[READ] List recorded, not-yet-applied undo tokens (most recent first)", clearly stating it is a read operation that lists undo tokens. This distinguishes it from the sibling tool undo_apply, which applies them. The verb 'list' and resource 'undo tokens' are specific and unambiguous.

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 when to use this tool: to see available undo tokens before applying them with undo_apply. It also provides guidance on the truncated flag, advising to re-run with higher limit when true. It does not explicitly state when not to use it, but the context is clear enough for an agent to decide.

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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