Blender Bridge

Undo Blender scene change

undo_last_action

Destructive

Undo the latest AI change that really changed the scene by returning the whole scene to the checkpoint saved before it. Later AI steps and manual edits since then are reverted too; the state before the undo is saved, so the undo itself can be reverted. With request_id, return to the state before that request. Tell the user what was reverted.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "undo_last_action"
  }
}

Input Schema

Name	Required	Description	Default
`request_id`	No	Operation whose before-state should be restored; omit to undo the latest scene-changing AI action.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 23, 2026

addedInput schema / properties / request_id / description

Added value: +"Operation whose before-state should be restored; omit to undo the latest scene-changing AI action."

First observedOct 23, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already mark this as destructive and non-idempotent, and the description adds genuinely new behavioral facts beyond them: later AI steps and manual edits since then are also reverted, the pre-undo state is saved so the undo is itself reversible, and it instructs the agent to report what was reverted. That is exactly the extra context an annotation cannot carry.

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?

Three tight sentences with the default behavior front-loaded, the request_id variant second, and an operational directive last. Every sentence carries information and nothing is redundant.

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?

With one optional parameter, full schema coverage, an output schema absent (but return values are state changes rather than data), and destructive/idempotent hints in annotations, the description covers everything an agent needs to invoke this correctly.

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?

Schema coverage is 100%, so the baseline is 3. The sentence 'With request_id, return to the state before that request' largely restates the schema's own parameter description ('Operation whose before-state should be restored'), adding little beyond it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('undo') and a precise object ('the latest AI change that really changed the scene'), plus the mechanism (restore the checkpoint saved before it). It doesn't explicitly name adjacent siblings like cancel_blender_operation or view_restore, but the scope is unambiguous on its own.

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 gives clear conditions for both modes: omit request_id to undo the latest scene-changing AI action, or pass request_id to return to the state before that specific request. There are no explicit exclusions or named alternatives, but the trigger context is clear.

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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