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CupOfOwls

Kroger MCP Server

list_departments

Retrieve all available departments in Kroger stores to browse products by category for grocery shopping.

Instructions

    Get a list of all available departments in Kroger stores.
    
    Returns:
        Dictionary containing department information
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'list_departments' tool. It fetches departments from the Kroger client, formats them, and returns a structured response with success status, count, and data.
    @mcp.tool()
    async def list_departments(ctx: Context = None) -> Dict[str, Any]:
        """
        Get a list of all available departments in Kroger stores.
        
        Returns:
            Dictionary containing department information
        """
        if ctx:
            await ctx.info("Getting list of departments")
        
        client = get_client_credentials_client()
        
        try:
            departments = client.location.list_departments()
            
            if not departments or "data" not in departments or not departments["data"]:
                return {
                    "success": False,
                    "message": "No departments found",
                    "data": []
                }
            
            # Format department data
            formatted_departments = [
                {
                    "department_id": dept.get("departmentId"),
                    "name": dept.get("name")
                }
                for dept in departments["data"]
            ]
            
            if ctx:
                await ctx.info(f"Found {len(formatted_departments)} departments")
            
            return {
                "success": True,
                "count": len(formatted_departments),
                "data": formatted_departments
            }
            
        except Exception as e:
            if ctx:
                await ctx.error(f"Error listing departments: {str(e)}")
            return {
                "success": False,
                "error": str(e),
                "data": []
            }
  • The registration call that adds the info_tools (including list_departments) to the MCP server.
    info_tools.register_tools(mcp)
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 the return type ('Dictionary containing department information') but fails to describe critical behaviors like whether this is a read-only operation, if it requires authentication, rate limits, error handling, or pagination. 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 brief and front-loaded with the main purpose in the first sentence. The second sentence about returns is useful but could be integrated more seamlessly. Overall, it's efficient with minimal waste, though minor improvements in flow could achieve a perfect score.

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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and return format but misses behavioral details like authentication needs or error cases. For a read operation in a retail context, more context would enhance completeness, but it meets the minimum viable threshold.

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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter details, so it appropriately focuses on the tool's function. A baseline of 4 is applied since no parameters exist, and the description doesn't attempt to explain non-existent inputs.

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 verb ('Get') and resource ('list of all available departments in Kroger stores'), making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'check_department_exists' or 'get_department_details', which would require a more detailed comparison to achieve a perfect score.

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 such as 'check_department_exists' or 'get_department_details'. It lacks context about prerequisites, timing, or exclusions, leaving the agent to infer usage based on the tool name alone.

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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