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fabric-aiops

undo_list

List pending undo tokens with verification status to safely review rollback actions before applying.

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``.

One extra row is fetched so ``truncated`` is **measured**, not guessed from
the returned count happening to equal the limit — a capped list that cannot
say "there is more" invites a weak model to report it as the whole story.

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).
    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 lists tokens most recent first, fetches one extra row to correctly indicate truncation, and each entry has 'effectVerified' with a detailed explanation of what false means. It also warns about lost responses making the reversal probable rather than confirmed.

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 with a clear summary followed by important behavioral notes. Each sentence adds value, avoiding fluff. It is thorough yet concise, fitting the necessary information in a readable format.

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?

While the description explains parameters and behavior, it lacks explicit mention of the output format (e.g., array of objects with fields like originalTool, inverseTool, note, effectVerified). Given no output schema, the description should fully specify the response structure to prevent ambiguity.

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 coverage is 0%, so the description must explain parameters. It clarifies 'limit' as max rows (default 50) and 'target' as unused (accepted for CLI uniformity). This adds value beyond the schema but could be more explicit about the exact type or range for 'limit'.

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 it lists recorded, not-yet-applied undo tokens (most recent first). It specifies the resource ('undo tokens') and the action ('list'), and distinguishes from the sibling tool '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 Guidelines5/5

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 view pending undo tokens before applying with undo_apply. It provides guidance on interpreting the results, such as the 'truncated' flag being measured via an extra fetched row and the 'effectVerified' field indicating reliability. It also notes the 'target' parameter is unused, preventing misuse.

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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