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kurenai-studio

blender-meta-mcp

blender_exec

Run Python code inside an open Blender instance to inspect or modify the scene, returning the result as JSON.

Instructions

Execute Python inside the running Blender (bpy), like ae_exec / cocosmcp_exec. The snippet runs on Blender's main thread with a VIEW_3D temp_override when one exists. bpy and mathutils are already in scope. The value of the last expression is returned as JSON (Vector/Euler/Color/Matrix become lists; bpy data-blocks become {type, name}). Assign result if the last line is not an expression. A top-level return is also accepted. Prefer the data API over bpy.ops. Do not return raw bpy objects. Successful calls push an undo step unless undo is false. Example: bpy.context.scene.name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesPython snippet. Last expression is the result.
undoNoPush an undo step before running. Defaults to true.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations, the description fully carries the behavioral burden and does so thoroughly: it discloses main-thread execution, temp_override behavior, return serialization, the need to assign result, acceptance of top-level return, and undo-step behavior. This is rich, concrete behavioral context that goes well beyond the schema.

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 compact yet information-dense, with the core action front-loaded and every subsequent sentence adding necessary execution semantics. The example at the end is useful without padding.

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 that there is no output schema and no annotations, the description covers everything an agent needs to invoke the tool correctly: execution context, scope, return formatting, result assignment, undo semantics, and an example. Nothing critical is missing for a general code-execution tool.

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 100%, so the baseline is 3, but the description adds meaningful nuance beyond the schema: the last expression becomes the returned value, result must be assigned for non-expression snippets, and the undo step is only pushed on successful calls unless undo is false. This goes beyond the simple 'defaults to true' schema note.

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 opening clause 'Execute Python inside the running Blender (bpy)' clearly states the verb and resource. It does not explicitly differentiate from the sibling tools blender_health, blender_viewport, or blender_scene_info, though the general-purpose nature is evident from the first sentence.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives strong coding guidance ('Prefer the data API over bpy.ops', 'Do not return raw bpy objects') and explains execution context. However, it never states when to choose blender_exec over the sibling tools or when not to use it, so alternative-selection guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.