Run Python code in Blender with preloaded modules like bpy, bmesh, and mathutils, and retrieve a variable's value via return_var for scripting and automation tasks.
在 Blender 里执行任意 Python（预置 bpy/bmesh/mathutils/Vector/math）。返回 stdout；return_var 指定要取回的变量名。
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| return_var | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden of behavioral disclosure. It states that it executes arbitrary Python and returns stdout/return_var, but it does not disclose that execution can have arbitrary side effects on the Blender scene, that changes may be irreversible, or that it could be unsafe. A powerful tool like this needs explicit caution about side effects and potential for irreversible modifications.
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 a single, front-loaded sentence that efficiently conveys the core purpose and return behavior. It wastes no words and is easy to scan. However, it omits critical details that could be added without losing conciseness, such as a cautionary note or usage context.
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?
For a tool that executes arbitrary code, the description is severely incomplete. It lacks error-handling behavior, examples of usage, implications for scene state, and safety warnings. With no output schema and no annotations, an agent has no guidance on how to construct valid code, interpret return values, or avoid unintended consequences. This is a high-complexity tool that demands much richer documentation.
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 description coverage is 0%, so the description must compensate for both parameters. It explains 'return_var' as specifying the variable to retrieve, but it does not explain how the 'code' parameter should be formatted, whether it is a raw string, how to access preloaded modules, or what happens if the variable does not exist. The description provides minimal added meaning beyond the schema's type definitions.
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 clearly states the verb 'execute' and the resource 'arbitrary Python in Blender' with preloaded modules (bpy/bmesh/mathutils/Vector/math). It also mentions the return behavior, distinguishing it from sibling tools that perform specific operations. The purpose is unambiguous and unique among siblings.
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?
No guidance is given on when to use this tool versus the many specific sibling tools. It does not state that it should be used as a fallback for operations not covered by other tools, nor does it caution against using it when a specialized tool exists. The description leaves the usage context entirely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.