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search_sketchfab_models

Search Sketchfab for 3D models using queries and filters to find downloadable assets for Blender projects.

Instructions

Search for models on Sketchfab with optional filtering.

Parameters:

  • query: Text to search for

  • categories: Optional comma-separated list of categories

  • count: Maximum number of results to return (default 20)

  • downloadable: Whether to include only downloadable models (default True)

Returns a formatted list of matching models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
categoriesNo
countNo
downloadableNo

Implementation Reference

  • MCP tool handler for searching Sketchfab models. Connects to Blender, sends search command with parameters, handles errors, and formats the list of models with details like name, UID, author, license, face count, and downloadability.
    @mcp.tool()
    def search_sketchfab_models(
        ctx: Context,
        query: str,
        categories: str = None,
        count: int = 20,
        downloadable: bool = True
    ) -> str:
        """
        Search for models on Sketchfab with optional filtering.
        
        Parameters:
        - query: Text to search for
        - categories: Optional comma-separated list of categories
        - count: Maximum number of results to return (default 20)
        - downloadable: Whether to include only downloadable models (default True)
        
        Returns a formatted list of matching models.
        """
        try:
            
            blender = get_blender_connection()
            logger.info(f"Searching Sketchfab models with query: {query}, categories: {categories}, count: {count}, downloadable: {downloadable}")
            result = blender.send_command("search_sketchfab_models", {
                "query": query,
                "categories": categories,
                "count": count,
                "downloadable": downloadable
            })
            
            if "error" in result:
                logger.error(f"Error from Sketchfab search: {result['error']}")
                return f"Error: {result['error']}"
            
            # Safely get results with fallbacks for None
            if result is None:
                logger.error("Received None result from Sketchfab search")
                return "Error: Received no response from Sketchfab search"
                
            # Format the results
            models = result.get("results", []) or []
            if not models:
                return f"No models found matching '{query}'"
                
            formatted_output = f"Found {len(models)} models matching '{query}':\n\n"
            
            for model in models:
                if model is None:
                    continue
                    
                model_name = model.get("name", "Unnamed model")
                model_uid = model.get("uid", "Unknown ID")
                formatted_output += f"- {model_name} (UID: {model_uid})\n"
                
                # Get user info with safety checks
                user = model.get("user") or {}
                username = user.get("username", "Unknown author") if isinstance(user, dict) else "Unknown author"
                formatted_output += f"  Author: {username}\n"
                
                # Get license info with safety checks
                license_data = model.get("license") or {}
                license_label = license_data.get("label", "Unknown") if isinstance(license_data, dict) else "Unknown"
                formatted_output += f"  License: {license_label}\n"
                
                # Add face count and downloadable status
                face_count = model.get("faceCount", "Unknown")
                is_downloadable = "Yes" if model.get("isDownloadable") else "No"
                formatted_output += f"  Face count: {face_count}\n"
                formatted_output += f"  Downloadable: {is_downloadable}\n\n"
            
            return formatted_output
        except Exception as e:
            logger.error(f"Error searching Sketchfab models: {str(e)}")
            import traceback
            logger.error(traceback.format_exc())
            return f"Error searching Sketchfab models: {str(e)}"
  • Input schema and documentation for the search_sketchfab_models tool, defining parameters query (str), categories (str optional), count (int default 20), downloadable (bool default True), and output as formatted str.
    """
    Search for models on Sketchfab with optional filtering.
    
    Parameters:
    - query: Text to search for
    - categories: Optional comma-separated list of categories
    - count: Maximum number of results to return (default 20)
    - downloadable: Whether to include only downloadable models (default True)
    
    Returns a formatted list of matching models.
    """
  • Registration of the tool via the @mcp.tool() decorator.
    @mcp.tool()
  • Usage instruction in the asset_creation_strategy prompt recommending search_sketchfab_models for finding Sketchfab models.
    - For objects/models: First search using search_sketchfab_models() with your query
    - Then download specific models using download_sketchfab_model() with the UID
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions that it 'Returns a formatted list of matching models,' which gives some output context, but lacks details on authentication needs, rate limits, error handling, pagination, or what 'formatted' entails. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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 well-structured and concise. It starts with a clear purpose statement, then lists parameters with brief explanations, and ends with return information. Every sentence earns its place with no wasted words, and it's appropriately sized for a 4-parameter search tool.

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 (search with filtering), no annotations, and no output schema, the description is partially complete. It covers parameters well but lacks behavioral context like authentication, rate limits, or detailed output format. For a tool with no structured safety or output information, it should provide more guidance on these aspects.

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?

Schema description coverage is 0%, so the description must compensate. It provides clear semantic explanations for all 4 parameters: 'query' as 'Text to search for,' 'categories' as 'Optional comma-separated list of categories,' 'count' as 'Maximum number of results to return (default 20),' and 'downloadable' as 'Whether to include only downloadable models (default True).' This adds substantial value beyond the bare schema.

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: 'Search for models on Sketchfab with optional filtering.' This specifies the verb ('search'), resource ('models on Sketchfab'), and scope ('with optional filtering'). However, it doesn't explicitly differentiate from sibling tools like 'search_polyhaven_assets' 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 sibling tools like 'search_polyhaven_assets' for different platforms or 'download_sketchfab_model' for subsequent actions. There's no context about prerequisites, typical use cases, or exclusions.

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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