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agora_get_product_detail

Retrieve detailed product information from Agora MCP by providing a product slug, enabling AI assistants to access comprehensive product data for user queries.

Instructions

Get details for a specific product in Agora.

Args:
    slug: The product slug, it usually looks something like 'royal-blue-waxed-shoe-laces-6f2049ef-0d08-4a79-8937-025bb596092f-1718242165922'
    
Returns:
    The product details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes

Implementation Reference

  • The main handler function for the 'agora_get_product_detail' tool. It uses the Agora client to fetch product details by slug and handles the response.
    @mcp.tool()
    async def agora_get_product_detail(slug: str) -> Dict:
        """
        Get details for a specific product in Agora.
        
        Args:
            slug: The product slug, it usually looks something like 'royal-blue-waxed-shoe-laces-6f2049ef-0d08-4a79-8937-025bb596092f-1718242165922'
            
        Returns:
            The product details.
        """
        response = get_agora().get_product_detail(slug=slug)
        return handle_response(response)
  • Helper function to lazily initialize and return the Agora client instance, used by the tool handlers.
    def get_agora():
        """Get or create an Agora instance. 
        We want to create the class instance inside the tool, 
        so the init errors will bubble up to the tool and hence the MCP client instead of silently failing
        during the server creation.
        """
    
        return Agora()
  • Helper function to process responses from Agora API calls, handling both raw HTTP responses and processed data.
    def handle_response(response):
        """
        Handle responses from Agora methods.
        """
        if hasattr(response, 'status_code'):
            # This is a raw response object
            try: return response.status_code, response.json()
            except: return response.status_code, response.text
        # This is already processed data (like a dictionary)
        return response
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 only states that it 'Get details' without specifying whether this is a read-only operation, if it requires authentication, what rate limits apply, or what happens on errors. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 sized and front-loaded, with the core purpose stated first, followed by parameter and return details. Every sentence adds value, though the 'Returns' section is vague ('The product details') and could be more specific. Overall, it's efficient with minimal waste.

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 low complexity (1 parameter, no nested objects) but lack of annotations and output schema, the description is moderately complete. It covers the purpose and parameter semantics adequately but fails to provide behavioral context or detailed return information. For a simple read operation, this is acceptable but leaves gaps in usage and transparency.

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 for the single parameter 'slug' by providing an example format ('royal-blue-waxed-shoe-laces-6f2049ef-0d08-4a79-8937-025bb596092f-1718242165922'), which clarifies its expected structure beyond the schema's basic string type. With 0% schema description coverage, this compensation is effective, though it doesn't explain where to obtain the slug or validate it.

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: 'Get details for a specific product in Agora.' It uses a specific verb ('Get') and resource ('product details'), making the intent unambiguous. However, it doesn't differentiate this from sibling tools like 'agora_search' or 'agora_get_order', which could also retrieve product-related information in different contexts.

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 this tool is appropriate (e.g., for retrieving full details of a known product) or when not to use it (e.g., for searching products or fetching order-related product info). With siblings like 'agora_search' available, this lack of differentiation leaves usage unclear.

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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