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search_polyhaven_assets

Search Poly Haven assets like HDRIs, textures, and 3D models for use in Blender projects, with filtering by type and categories to find suitable resources.

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

  • The handler function for the 'search_polyhaven_assets' MCP tool. It is decorated with @mcp.tool() for registration. The function receives parameters, sends a command to the Blender connection to perform the search, and formats the results into a readable string output.
    @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?

With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions optional filtering and returns 'basic information,' but doesn't disclose pagination, rate limits, authentication needs, error conditions, or what constitutes 'basic information' in the response. This is inadequate for a search 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.

Conciseness4/5

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

The description is appropriately concise with three sentences: purpose statement, parameter explanations, and return value note. It's front-loaded with the core function, though the parameter list could be integrated more smoothly. No wasted words, but structure is basic.

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

Completeness2/5

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

Given the tool's complexity (search with filtering), lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't explain the response format, error handling, or usage constraints, leaving significant gaps for an AI agent to operate effectively.

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?

The description adds some semantic context beyond the schema: it explains that 'asset_type' accepts specific values (hdris, textures, models, all) and that 'categories' is comma-separated. However, with 0% schema description coverage and 2 parameters, this only partially compensates—it doesn't clarify category format or provide examples. Baseline is 3 since it adds meaningful but incomplete details.

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+resource combination. It distinguishes from obvious siblings like 'download_polyhaven_asset' by focusing on search rather than retrieval, though it doesn't explicitly differentiate from 'search_sketchfab_models' beyond the platform name.

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 when to prefer this over 'search_sketchfab_models' for different asset types or platforms, nor does it specify prerequisites or contextual triggers for initiating a search.

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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