Skip to main content
Glama
opslon

BlenderMCP

by opslon

download_sketchfab_model

Download and import Sketchfab 3D models into Blender with automatic scaling to specified dimensions for accurate scene integration.

Instructions

Download and import a Sketchfab model by its UID. The model will be scaled so its largest dimension equals target_size.

Parameters:

  • uid: The unique identifier of the Sketchfab model

  • target_size: REQUIRED. The target size in Blender units/meters for the largest dimension. You must specify the desired size for the model. Examples: - Chair: target_size=1.0 (1 meter tall) - Table: target_size=0.75 (75cm tall) - Car: target_size=4.5 (4.5 meters long) - Person: target_size=1.7 (1.7 meters tall) - Small object (cup, phone): target_size=0.1 to 0.3

Returns a message with import details including object names, dimensions, and bounding box. The model must be downloadable and you must have proper access rights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uidYes
target_sizeYes

Implementation Reference

  • The `download_sketchfab_model` function acts as the MCP tool handler. It is registered via `@mcp.tool()` and communicates with Blender via `blender.send_command`.
    @mcp.tool()
    def download_sketchfab_model(
        ctx: Context,
        uid: str,
        target_size: float
    ) -> str:
        """
        Download and import a Sketchfab model by its UID.
        The model will be scaled so its largest dimension equals target_size.
        
        Parameters:
        - uid: The unique identifier of the Sketchfab model
        - target_size: REQUIRED. The target size in Blender units/meters for the largest dimension.
                      You must specify the desired size for the model.
                      Examples:
                      - Chair: target_size=1.0 (1 meter tall)
                      - Table: target_size=0.75 (75cm tall)
                      - Car: target_size=4.5 (4.5 meters long)
                      - Person: target_size=1.7 (1.7 meters tall)
                      - Small object (cup, phone): target_size=0.1 to 0.3
        
        Returns a message with import details including object names, dimensions, and bounding box.
        The model must be downloadable and you must have proper access rights.
        """
        try:
            blender = get_blender_connection()
            logger.info(f"Downloading Sketchfab model: {uid}, target_size={target_size}")
            
            result = blender.send_command("download_sketchfab_model", {
                "uid": uid,
                "normalize_size": True,  # Always normalize
                "target_size": target_size
            })
            
            if result is None:
                logger.error("Received None result from Sketchfab download")
                return "Error: Received no response from Sketchfab download request"
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by disclosing key behaviors: it downloads and imports, scales the model, requires access rights, and returns import details. It doesn't cover potential errors, rate limits, or exact output format, but adds substantial value beyond basic purpose.

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 well-structured with a clear purpose statement, parameter explanations, and return details. It's front-loaded with key information, though the examples for 'target_size' are slightly verbose but still useful. Every sentence adds value without waste.

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 no annotations and no output schema, the description provides good context for a 2-parameter tool: it explains what the tool does, parameters, returns, and prerequisites. It could improve by detailing error cases or exact return structure, but it's largely complete for its complexity.

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

Parameters5/5

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 fully. It does so by clearly explaining both parameters: 'uid' as the unique identifier and 'target_size' as required with detailed examples and units. This adds significant meaning beyond the bare 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 clearly states the specific action ('Download and import'), the resource ('a Sketchfab model by its UID'), and the key behavior ('scaled so its largest dimension equals target_size'). It distinguishes itself from siblings like 'search_sketchfab_models' by focusing on downloading/importing rather than searching.

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 implies usage when you have a specific model UID and want to import it with size control, but it doesn't explicitly state when to use this tool versus alternatives like 'import_generated_asset' or prerequisites beyond access rights. It mentions 'The model must be downloadable and you must have proper access rights,' which provides some context but not explicit alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/opslon/blender-mcp-optimized'

If you have feedback or need assistance with the MCP directory API, please join our Discord server