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search_sketchfab_models

Find and filter 3D models from Sketchfab for use in Blender projects, enabling AI-assisted modeling with downloadable assets.

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

  • The core handler function for the 'search_sketchfab_models' tool. It is decorated with @mcp.tool() for registration and @telemetry_tool for logging. The function sends a 'search_sketchfab_models' command to the Blender addon with the provided parameters, processes the response, handles errors, and formats a readable list of matching models including name, UID, author, license, face count, and download status.
    @telemetry_tool("search_sketchfab_models")
    @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)}"
  • The docstring provides the input schema (parameters description) and output description for the tool.
    """
    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.
    """
  • The @mcp.tool() decorator registers the function as an MCP tool.
    @mcp.tool()
  • Usage guidance for the tool within the asset_creation_strategy prompt.
    Sketchfab is good at Realistic models, and has a wider variety of models than PolyHaven.
    Use get_sketchfab_status() to verify its status
    If Sketchfab is enabled:
    - For objects/models: First search using search_sketchfab_models() with your query
    - Then download specific models using download_sketchfab_model() with the UID
    - Note that only downloadable models can be accessed, and API key must be properly configured
    - Sketchfab has a wider variety of models than PolyHaven, especially for specific subjects
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 that the tool 'Returns a formatted list of matching models,' which gives some output information, but it lacks details on rate limits, authentication needs, error handling, pagination, or what 'formatted' entails. For a search tool with no 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 appropriately sized and front-loaded. The first sentence states the purpose clearly, followed by a bulleted list of parameters with concise explanations. There is no wasted text, and every sentence earns its place by adding necessary information.

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 (4 parameters, no output schema, no annotations), the description is partially complete. It covers parameters well but lacks behavioral details like rate limits or error handling. Without an output schema, it should explain return values more thoroughly than just 'formatted list.' It's adequate as a minimum viable description but has clear gaps.

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 adds meaningful semantics for all four 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 provides clear context beyond the bare schema, though it doesn't explain category values or result formatting 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: 'Search for models on Sketchfab with optional filtering.' It specifies the verb ('search'), resource ('models on Sketchfab'), and scope ('with optional filtering'). However, it doesn't explicitly distinguish this tool from its sibling 'search_polyhaven_assets' beyond the platform name, which is why it doesn't reach a score of 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 doesn't mention sibling tools like 'search_polyhaven_assets' or 'download_sketchfab_model', nor does it specify prerequisites, context, or exclusions. The only implied usage is for searching Sketchfab models, but this is redundant with the purpose statement.

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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