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import_tripo_glb_model

Import GLB 3D models from URLs directly into Blender scenes using the Tripo MCP Server. This tool enables AI assistants to add 3D assets to Blender projects.

Instructions

Import a GLB model from URL into Blender scene

Parameters:
- glb_url: Download URL of the GLB model file

Returns:
Result message of the import operation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
glb_urlYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function decorated with @mcp.tool() that implements the 'import_tripo_glb_model' tool. It sends the import command to the Blender addon via socket connection and formats the response listing imported models with their dimensions.
    @mcp.tool()
    def import_tripo_glb_model(ctx: Context, glb_url: str) -> str:
        """
        Import a GLB model from URL into Blender scene
    
        Parameters:
        - glb_url: Download URL of the GLB model file
    
        Returns:
        Result message of the import operation
        """
        try:
            blender = get_blender_connection()
            result = blender.send_command("import_tripo_glb_model", {"url": glb_url})
    
            if "error" in result:
                return f"Import failed: {result['error']}"
    
            if result.get("status") == "success":
                output = ["Successfully imported models:"]
                for model in result.get("models", []):
                    dim = model["dimensions"]
                    output.append(
                        f"• {model['name']} | Dimensions: "
                        f"{dim['x']} x {dim['y']} x {dim['z']} meters"
                    )
    
                if not output:
                    output.append("No models found in imported file")
    
                return "\n".join(output)
            else:
                return f"Import failed: {result.get('message', 'Unknown error')}"
    
        except Exception as e:
            logger.error(f"Error importing GLB model: {str(e)}")
            return f"GLB model import failed: {str(e)}"
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Import') and return type ('Result message'), but lacks details on permissions, side effects (e.g., scene modification), error handling, or operational constraints like rate limits. This is inadequate for a mutation tool with zero annotation coverage.

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

Conciseness5/5

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

The description is front-loaded with the core purpose, followed by clear sections for parameters and returns. It uses minimal sentences with zero waste, making it efficient and easy to parse for an agent.

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?

Given the tool's complexity (a mutation with 1 parameter), no annotations, and an output schema (which covers return values), the description is partially complete. It explains the purpose and parameter but lacks behavioral context like side effects or usage guidelines, leaving gaps that could hinder agent selection.

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?

The description explicitly lists the parameter 'glb_url' and explains it as 'Download URL of the GLB model file', adding meaningful context beyond the schema's basic title. With 0% schema description coverage and only 1 parameter, this compensates well, though it could specify URL format or constraints.

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 verb ('Import') and resource ('GLB model from URL into Blender scene'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'create_3d_model_from_image' or 'download_polyhaven_asset', which might also involve 3D model handling, so it misses full sibling distinction.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., when importing vs. creating models), or exclusions, leaving the agent to infer usage from the purpose alone.

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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