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construct_object

Creates any 3D object from natural language descriptions using AI-generated Blender scripts. Supports multiple complexity levels and style presets.

Instructions

PORTMANTEAU PATTERN RATIONALE: Consolidates natural language 3D construction into single agentic interface. Prevents tool explosion while enabling infinite 3D creativity through LLM-generated Blender scripts. Follows FastMCP 2.14.3 best practices.

Universal 3D object construction using natural language and LLM-generated Blender scripts.

This revolutionary tool enables creation of any 3D object through natural language descriptions by leveraging FastMCP 2.14.3 sampling to request SOTA LLM generation of Blender Python code.

Agentic Workflow:

  1. Analysis: Parse natural language description and scene context

  2. Sampling Request: Ask MCP client (SOTA LLM) to generate Blender Python script

  3. Code Generation: LLM creates production-ready Blender automation code

  4. Validation: Security and syntax validation of generated code

  5. Execution: Safe execution in Blender with error handling

  6. Iteration: Request refinements if needed (up to max_iterations)

Supported Complexity Levels:

  • simple: Basic primitives, basic transforms, simple materials

  • standard: Complex meshes, modifiers, materials, basic animation

  • complex: Advanced geometry, rigging, physics, complex materials/textures

Style Presets:

  • realistic: Physically accurate materials, lighting, proportions

  • stylized: Artistic interpretation, exaggerated features, cartoon-like

  • lowpoly: Minimal geometry, optimized for performance

  • scifi: Futuristic design, metallic materials, glowing effects

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ctxYesFastMCP context for sampling and conversational responses
descriptionNoNatural language description of object to create Examples: "a robot like Robbie from Forbidden Planet", "a medieval castle", "a sleek sports car"a simple cube
nameNoName for the created object in Blender scene. Default: "ConstructedObject"ConstructedObject
complexityNoComplexity level for script generation. One of: "simple", "standard", "complex". Default: "standard". Affects detail level and operation complexity.standard
style_presetNoOptional style guidance. One of: "realistic", "stylized", "lowpoly", "scifi". Default: None (let LLM decide based on description).
reference_objectsNoExisting Blender objects to use as reference for style/consistency. Default: None. LLM will analyze these objects for consistent styling.
allow_modificationsNoWhether LLM can modify existing scene objects. Default: True. Set to False for conservative construction that only adds new objects.
max_iterationsNoMaximum refinement iterations if initial script fails. Default: 3. Higher values allow more complex objects but increase processing time.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description takes on the burden of disclosing behavior. It details the workflow, security validation, error handling, iteration, and that modifications can be limited via allow_modifications. However, it does not mention potential side effects on the existing scene beyond modifications, nor does it specify if the tool always creates a new object or may replace existing ones.

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

Conciseness3/5

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

The description is fairly long, including a rationale and workflow that could be more concise. However, it is well-structured with bullet points and clear sections. Some redundancy exists, such as repeating 'FastMCP 2.14.3 best practices' and the workflow list. It earns its length by covering multiple aspects, but could be trimmed without losing clarity.

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

Completeness4/5

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

Given the tool's complexity (8 parameters, no annotations, output schema exists), the description covers the main workflow, supported complexity levels, style presets, and iteration behavior. It explains how parameters like allow_modifications and max_iterations affect operation. It does not address error cases or limitations in depth, but the output schema likely covers return values, so this is acceptable.

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

Parameters3/5

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

Since schema description coverage is 100%, the description adds limited new meaning beyond the schema. It does provide context such as complexity levels and style preset definitions, but these are already implied by the schema parameter names and descriptions. The description helps by explaining the workflow in relation to parameters, but does not significantly enhance understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose: 'Universal 3D object construction using natural language and LLM-generated Blender scripts.' It distinguishes itself from sibling tools like generate_blender_script by being a high-level agentic interface that handles execution and iteration, not just script generation.

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 outlines a workflow and mentions iteration, but lacks explicit guidance on when to use this tool versus alternatives like 'generate_blender_script' or 'manage_object_construction'. It does not state scenarios where this tool is preferred or avoided.

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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