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search_polyhaven_assets

Search and filter 3D assets from Polyhaven for use in Blender projects. Find HDRI environments, textures, and models by type and category to enhance 3D scenes.

Instructions

Search for assets on Polyhaven with optional filtering.

Parameters:
- asset_type: Type of assets to search for (hdris, textures, models, all)
- categories: Optional comma-separated list of categories to filter by

Returns a list of matching assets with basic information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asset_typeNoall
categoriesNo

Implementation Reference

  • Handler function for the search_polyhaven_assets tool. It is registered via the @mcp.tool() decorator and implements the tool logic by sending a command to the Blender addon via the persistent connection, then formatting and returning the results.
    @mcp.tool()
    def search_polyhaven_assets(
        ctx: Context,
        asset_type: str = "all",
        categories: str = None
    ) -> str:
        """
        Search for assets on Polyhaven with optional filtering.
        
        Parameters:
        - asset_type: Type of assets to search for (hdris, textures, models, all)
        - categories: Optional comma-separated list of categories to filter by
        
        Returns a list of matching assets with basic information.
        """
        try:
            blender = get_blender_connection()
            result = blender.send_command("search_polyhaven_assets", {
                "asset_type": asset_type,
                "categories": categories
            })
            
            if "error" in result:
                return f"Error: {result['error']}"
            
            # Format the assets in a more readable way
            assets = result["assets"]
            total_count = result["total_count"]
            returned_count = result["returned_count"]
            
            formatted_output = f"Found {total_count} assets"
            if categories:
                formatted_output += f" in categories: {categories}"
            formatted_output += f"\nShowing {returned_count} assets:\n\n"
            
            # Sort assets by download count (popularity)
            sorted_assets = sorted(assets.items(), key=lambda x: x[1].get("download_count", 0), reverse=True)
            
            for asset_id, asset_data in sorted_assets:
                formatted_output += f"- {asset_data.get('name', asset_id)} (ID: {asset_id})\n"
                formatted_output += f"  Type: {['HDRI', 'Texture', 'Model'][asset_data.get('type', 0)]}\n"
                formatted_output += f"  Categories: {', '.join(asset_data.get('categories', []))}\n"
                formatted_output += f"  Downloads: {asset_data.get('download_count', 'Unknown')}\n\n"
            
            return formatted_output
        except Exception as e:
            logger.error(f"Error searching Polyhaven assets: {str(e)}")
            return f"Error searching Polyhaven assets: {str(e)}"
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Returns a list of matching assets with basic information,' which hints at read-only behavior and output format, but lacks details on rate limits, authentication needs, pagination, or error handling. For a search tool with zero annotation coverage, this is insufficient.

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 parameter explanations and return information in a structured format. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 moderate complexity (2 parameters, no output schema, no annotations), the description covers the basic purpose and parameters adequately but lacks depth in usage guidelines, behavioral traits, and output details. It is minimally viable but has clear gaps in providing a complete context for effective tool 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 adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'asset_type' includes options like 'hdris, textures, models, all' and 'categories' is an 'optional comma-separated list,' clarifying usage that the schema alone does not provide. With only 2 parameters, this compensates well for the schema gap.

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 tool's purpose as 'Search for assets on Polyhaven with optional filtering,' which is a specific verb (search) and resource (assets on Polyhaven). However, it does not explicitly distinguish itself from sibling tools like 'search_sketchfab_models' or 'get_polyhaven_categories,' which limits the score from a perfect 5.

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 mentions optional filtering but does not specify scenarios, prerequisites, or exclusions, such as when to prefer 'search_sketchfab_models' for models or 'get_polyhaven_categories' for category lists. This lack of contextual direction results in a low score.

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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