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endpoint-aiops-mcp

endpoint-aiops-mcp

by AIops-tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

undo_list

Read-only

Lists recorded, not-yet-applied undo tokens so you can review pending reversals, check unconfirmed effects, and pass their undoId to 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.

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 (1..500, default 50). target: Unused (undo state is host-local); accepted for CLI uniformity.

Returns dict: {undos[], returned, limit, truncated}. One extra row is fetched, so 'truncated' is measured rather than guessed from the returned count happening to equal the limit; re-run with a higher limit when true.

Input Schema

NameRequiredDescriptionDefault
limitNo
targetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true, and the [READ] marker is consistent with that. Beyond annotations, it discloses the critical effectVerified semantic — false means the reversed change is PROBABLE, not confirmed — plus the 'one extra row fetched' truncation mechanism. This is exactly the behavioral context an agent needs.

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?

Front-loaded with the [READ] tag and purpose, then Args/Returns sections. Nearly every sentence earns its place, though the effectVerified and truncation paragraphs are dense and could be marginally tightened.

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?

No output schema exists, yet the description specifies the return shape ({undos[], returned, limit, truncated}) and explains truncated semantics. Combined with the parameter and behavioral notes, an agent has everything needed to call and interpret this tool.

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%, so the description carries the full burden and does so: limit is documented with range (1..500) and default (50), and target is explicitly disclosed as unused/CLI-uniformity-only. No parameter is left ambiguous.

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?

States a specific verb and resource ('List recorded, not-yet-applied undo tokens') plus ordering ('most recent first'), which immediately distinguishes it from the sibling undo_apply that consumes the tokens. An agent can tell what this returns without opening the schema.

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?

Explicitly routes the agent: 'Use the undoId with undo_apply,' naming the sibling that performs the action. It also gives a conditional: when effectVerified is false, check live state before applying. This is explicit when-to-use and how-to-proceed guidance.

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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