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Turbify Store MCP Server

by benpeke

get_catalog_items

Retrieve detailed information for multiple catalog items by specifying their IDs through the Turbify Store Catalog API.

Instructions

    Get details for multiple catalog items.
    
    Args:
        item_ids: List of item IDs to retrieve
    
    Returns:
        JSON string with item details
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
item_idsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main execution logic for the 'get_catalog_items' tool. It fetches item details using the TurbifyStoreAPIClient, serializes the response, and returns a JSON string.
    @mcp.tool()
    def get_catalog_items(item_ids: List[str]) -> str:
        """
        Get details for multiple catalog items.
        
        Args:
            item_ids: List of item IDs to retrieve
        
        Returns:
            JSON string with item details
        """
        try:
            response = client.get_item_details(item_ids)
            
            # Convert items to dictionaries if they're not already
            items = response.items if response.items else []
            serializable_items = []
            for item in items:
                if item and not isinstance(item, dict):
                    # If it's a Pydantic model, convert to dict
                    if hasattr(item, 'dict'):
                        serializable_items.append(item.dict())
                    # If it's a dataclass, convert to dict
                    elif hasattr(item, '__dict__'):
                        serializable_items.append(item.__dict__)
                    # Otherwise, try to serialize as is
                    else:
                        serializable_items.append(dict(item))
                else:
                    serializable_items.append(item)
            
            return json.dumps({
                "status": response.status,
                "success": response.is_success,
                "messages": response.success_messages,
                "errors": response.error_messages,
                "item_count": len(serializable_items),
                "items": serializable_items
            }, indent=2)
            
        except APIError as e:
            return json.dumps({
                "status": "error",
                "success": False,
                "errors": [str(e)],
                "item_ids": item_ids
            }, indent=2)
  • The call to register_catalog_tools(mcp) which defines and registers the get_catalog_items tool using the @mcp.tool() decorator.
    register_catalog_tools(mcp)
  • The registration function that sets up the API client and defines the tool functions with @mcp.tool() decorators, including get_catalog_items.
    def register_catalog_tools(mcp):
        """Register catalog management tools with the MCP server."""
        
        # Initialize client (will be reused across tool calls)
        client = TurbifyStoreAPIClient()
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 tool retrieves details but doesn't mention whether this is a read-only operation, if it requires authentication, potential rate limits, error conditions, or how it handles invalid IDs. This leaves significant gaps in understanding the tool's behavior and constraints.

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 extremely concise. It uses a clear header format with 'Args' and 'Returns' sections, contains no redundant information, and every sentence directly contributes to understanding the tool's functionality without any wasted words.

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 has an output schema (which handles return value documentation) and only one parameter, the description covers the basics adequately. However, as a retrieval tool with sibling alternatives and no annotations, it should ideally provide more context about when to use it versus other search tools and any behavioral constraints.

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 schema description coverage is 0%, so the description must compensate. It clearly explains that 'item_ids' is a 'List of item IDs to retrieve', adding essential meaning beyond the bare schema. However, it doesn't provide examples of valid ID formats, constraints on list size, or whether IDs must exist, leaving some ambiguity.

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 with a specific verb ('Get details') and resource ('multiple catalog items'), making it immediately understandable. However, it doesn't explicitly distinguish this tool from its sibling 'search_catalog_items' or 'advanced_search_catalog_items', which likely have overlapping functionality for retrieving catalog items.

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 like 'search_catalog_items' or 'advanced_search_catalog_items'. It mentions retrieving items by IDs but doesn't specify prerequisites, limitations, or typical use cases, leaving the agent with insufficient context for optimal tool selection.

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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