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rupa3d

rupa3d

by tiranyx
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rupa_skrip

Destructive

Execute Python bpy scripts in headless Blender to build parametric 3D scenes. Use named numbers for adjustable geometry and retrieve the output dictionary.

Instructions

PRIMITIF PEMODELAN: jalankan Python bpy di dalam Blender headless, lalu simpan adegannya. Tersedia: bpy, bmesh, math, mathutils, RUANG (jalur ruang kerja), dan dict keluaran yang ikut dikembalikan. Tulis bentuk dari ANGKA BERNAMA, bukan nilai ajaib — itu yang membuatnya bisa diubah lagi tanpa dibangun ulang.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "kode": "<kode>"
    },
    "name": "rupa_skrip"
  }
}

Input Schema

NameRequiredDescriptionDefault
kodeYes
ruangNoNama ruang kerja; tiap ruang punya adegan.blend sendiri.utama

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.6.2

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already supply destructiveHint=true and readOnlyHint=false, covering the main safety profile. The description adds useful context: execution is headless, the scene is saved afterward, and a `keluaran` dict is returned. It does not discuss failure behavior or partial mutation, but the destructive annotation and save side-effect are the most important traits.

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?

The description is compact: it front-loads the purpose, then lists the available environment, then gives coding guidance. Each sentence earns its place, with no filler. It is slightly run-on, but still scannable and appropriately sized.

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 a complex code-execution tool with no output schema, it covers many key aspects: available modules, workspace path, return dict, and save behavior. It does not specify what happens on error, whether the scene is loaded before execution, or the expected shape of `keluaran`. This leaves enough ambiguity that an agent may need to probe or infer before calling.

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 only 50%, and the required `kode` parameter has no schema description. The description compensates by explaining what code can reference (bpy, bmesh, math, mathutils, RUANG, keluaran) and gives concrete guidance about using named numbers instead of magic values. The `ruang` parameter is already described in the schema, so the added context is meaningful.

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 clearly states the core operation: run Python `bpy` inside headless Blender and save the scene. It also labels itself a low-level modeling primitive, which helps separate it from the many specialized sibling tools. It does not name a sibling, but the raw-script identity is specific enough.

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 'PRIMITIF PEMODELAN' label implies it is meant for custom, low-level modeling code, and the available bpy/bmesh/mathutils context suggests a general scripting use. However, there is no explicit statement of when to choose this tool over the specialized siblings, nor any exclusions. Usage guidance is implied rather than stated.

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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