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blender_run_python

Execute Python code inside a live Blender session with bpy available, returning stdout for automation, scripted edits, and scene inspection.

Instructions

Run Python inside Blender (bpy available). The reply is the code's stdout: use print().

Executes arbitrary code in the user's Blender session. Keep edits reproducible: record meaningful changes in your build scripts, not only in the live scene.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
timeoutNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses that the return channel is stdout and that code runs in the user's session with real side effects, plus a reproducibility caution. It does not explain the timeout parameter's behavior, error/exception reporting, or what happens on a hung session.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Key operational fact (return is stdout, use print) is front-loaded in the first sentence, with secondary guidance after. Two brief paragraphs with minor redundancy between 'Run Python inside Blender' and 'Executes arbitrary code in the user's Blender session.'

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an arbitrary-code-execution tool with no annotations, no output schema, and 0% schema coverage, the return mechanism being documented is genuinely valuable. However, the timeout parameter and failure/exception behavior are left entirely unexplained, leaving gaps an agent must discover empirically.

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

Parameters2/5

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

Schema coverage is 0% and the description compensates only partially: it clarifies the code parameter's expected behavior (output must be printed to stdout). The timeout parameter (default 60) is never explained — no units, no semantics for zero/exceeded values, no guidance on when to raise 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?

States a specific verb (run) and resource (Python inside Blender) and clarifies that bpy is available, so an agent knows this executes arbitrary script against the live Blender API. It does not explicitly contrast itself with the many specific siblings (blender_import_glb, blender_render, etc.), though its role as the general escape hatch is evident.

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

Usage Guidelines2/5

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

There is no explicit when-to-use-this-vs-a-specific-sibling guidance, which matters because there are 24 specialized sibling tools. The only guidance given is about reproducibility (record changes in build scripts), which is a best-practice note rather than a tool-selection rule.

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