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Propose reversing an applied proposal

undo_changes

Builds a new proposal that would reverse an applied one (removes the negatives it added, restores statuses and budgets). Changes nothing by itself: like any proposal, it must be approved and then applied with apply_changes.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "proposal_id": "<proposal_id>"
    },
    "name": "undo_changes"
  }
}

Input Schema

NameRequiredDescriptionDefault
proposal_idYesThe id of an applied proposal, from change_history or apply_changes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed1 schema field changed
    • addedInput schema / properties / proposal_id / description
      Added value: +"The id of an applied proposal, from change_history or apply_changes"
  3. First observed

TDQS

A4.5/5.0
Behavior5/5

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

The name 'undo_changes' implies immediate reversal, and the description corrects that misconception head-on: 'Changes nothing by itself: like any proposal, it must be approved and then applied with apply_changes.' It also discloses the concrete semantics of the reversal. This is context annotations cannot supply on their own.

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?

Two sentences, no filler, and the most important fact (it only builds a proposal) is placed immediately after the one-line purpose. Every clause earns its place.

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 an agent has everything needed: what the tool produces, that it is inert until approved and applied, and the required follow-up call. Nothing material is omitted for a single-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter and schema description coverage is 100%; the schema already tells the agent the id comes from change_history or apply_changes. The description adds no additional parameter meaning, so baseline 3 applies.

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 ('Builds a new proposal that would reverse an applied one') and enumerates the effects (removes negatives, restores statuses and budgets). This clearly separates it from propose_changes, apply_changes, and discard_proposal.

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?

Makes clear it targets an *applied* proposal and names the required follow-up path (approve, then apply_changes), which is exactly the routing an agent needs. It stops short of an explicit exclusion (e.g. use discard_proposal for a pending, un-applied proposal), so it's clear context rather than full when/when-not 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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