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nickweedon

Skeleton MCP Server

by nickweedon

get_item

Retrieve specific item data by providing its unique identifier. Use this tool to fetch detailed information about any item stored in the system when you have the item ID.

Instructions

Get a specific item by ID.

Args: item_id: The unique identifier of the item

Returns: The item data if found

Raises: ValueError: If the item is not found

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
item_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Implementation of the get_item tool handler. Retrieves a specific item by its ID from the MOCK_ITEMS dictionary, raises ValueError if not found. Includes type hints and comprehensive docstring describing input/output.
    async def get_item(item_id: str) -> dict[str, Any]:
        """
        Get a specific item by ID.
    
        Args:
            item_id: The unique identifier of the item
    
        Returns:
            The item data if found
    
        Raises:
            ValueError: If the item is not found
        """
        # In a real implementation:
        # client = get_client()
        # return client.get(f"items/{item_id}")
    
        if item_id not in MOCK_ITEMS:
            raise ValueError(f"Item not found: {item_id}")
    
        return MOCK_ITEMS[item_id]
  • Registration of the get_item tool using the FastMCP mcp.tool() decorator, making it available to MCP clients.
    mcp.tool()(example.get_item)
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns item data if found and raises a ValueError if not, adding useful context beyond basic functionality. However, it lacks details on permissions, rate limits, or error handling beyond the ValueError, which is a gap for a read operation.

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 front-loaded with the core purpose. It uses sections (Args, Returns, Raises) efficiently, with each sentence adding value. There's no wasted text, making it easy for an AI agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (single parameter, read-only operation) and the presence of an output schema, the description is reasonably complete. It covers the purpose, parameter semantics, and error behavior. However, it could improve by addressing usage relative to siblings or adding more behavioral context, which holds it back from a perfect score.

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 adds meaning by explaining that 'item_id' is 'The unique identifier of the item,' which clarifies the parameter's purpose beyond the schema's type definition. Since there's only one parameter, this is sufficient for high utility, though not exhaustive.

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 a specific item by ID.' It specifies the verb ('Get') and resource ('item'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'list_items' or 'health_check,' which prevents 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. It doesn't mention sibling tools like 'list_items' for multiple items or 'create_item'/'update_item' for modifications. Usage is implied by the name but not explicitly stated, leaving gaps for an AI agent.

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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