Run Python code in Blender's main thread to create, modify, or query scenes and objects, then return the script's standard output.
在 Blender 主线程执行一段 Python 代码（可直接使用 bpy、C、D），返回标准输出。
示例：创建立方体并移动 import bpy bpy.ops.mesh.primitive_cube_add(size=2, location=(1, 0, 0)) print('created:', bpy.context.object.name)
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It correctly notes main-thread execution and stdout return, but it does not mention that code can mutate scenes, that mutations persist, what happens on exceptions, or any Blender state prerequisites. For an arbitrary code execution tool, this is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core behavior, followed directly by a useful, concrete example. Every sentence and code line earns its place; there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although an output schema exists and the parameter surface is minimal, this is a high-complexity, potentially destructive tool with no annotations. The description omits side-effect warnings, error behavior, Blender startup/runtime requirements, and timeout or safety considerations. An agent could call it correctly for a trivial example, but not fully understand the consequences in real use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain the 'code' parameter. It does so beyond the schema by stating the code is Python, runs in the Blender main thread, can use bpy/C/D, and by providing a complete runnable example. It stops short of documenting error formats or execution limits, but the essential semantics are clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action: executing Python code in Blender's main thread, with access to bpy, C, and D, and returning standard output. This clearly distinguishes it from the sibling read-only tools like blender_status and blender_scene_summary, even without naming them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied: use this tool when arbitrary Blender Python code must run, as shown by the cube-creation example. However, the description does not explicitly say when to prefer this over the sibling tools, nor does it warn against using it for simple queries that those tools may serve.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.