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dynamodb_item_scan

Scan items in a DynamoDB table using filter expressions to retrieve specific data. This tool helps query and analyze table contents by applying custom search criteria.

Instructions

Scan items in a DynamoDB table

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYesName of the DynamoDB table
filter_expressionNoFilter expression
expression_attributesNo

Implementation Reference

  • Handler implementation for the dynamodb_item_scan tool: builds scan parameters from arguments and executes dynamodb_client.scan()
    elif name == "dynamodb_item_scan":
        scan_params = {"TableName": arguments["table_name"]}
    
        if "filter_expression" in arguments:
            scan_params["FilterExpression"] = arguments["filter_expression"]
    
            if "expression_attributes" in arguments:
                attrs = arguments["expression_attributes"]
                if "names" in attrs:
                    scan_params["ExpressionAttributeNames"] = attrs["names"]
                if "values" in attrs:
                    scan_params["ExpressionAttributeValues"] = attrs["values"]
    
        response = dynamodb_client.scan(**scan_params)
  • Input schema and Tool definition for dynamodb_item_scan, specifying parameters like table_name, filter_expression, and expression_attributes
    Tool(
        name="dynamodb_item_scan",
        description="Scan items in a DynamoDB table",
        inputSchema={
            "type": "object",
            "properties": {
                "table_name": {
                    "type": "string",
                    "description": "Name of the DynamoDB table"
                },
                "filter_expression": {
                    "type": "string",
                    "description": "Filter expression"
                },
                "expression_attributes": {
                    "type": "object",
                    "properties": {
                        "values": {
                            "type": "object",
                            "description": "Expression attribute values"
                        },
                        "names": {
                            "type": "object",
                            "description": "Expression attribute names"
                        }
                    }
                }
            },
            "required": ["table_name"]
        }
    ),
  • Tool registration via the list_tools handler, which returns get_aws_tools() including the dynamodb_item_scan tool definition from tools.py
    async def list_tools() -> list[Tool]:
        """List available AWS tools"""
        logger.debug("Handling list_tools request")
        return get_aws_tools()
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 scanning but fails to describe key behaviors: whether it's read-only or mutative, potential performance impacts (e.g., scanning entire tables), rate limits, or authentication needs. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational traits.

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 a single, efficient sentence that front-loads the core action ('Scan items') without unnecessary words. It's appropriately sized for the tool's complexity, with zero waste, making it highly concise and well-structured.

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

Completeness2/5

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

Given the tool's complexity (3 parameters, nested objects, no output schema, and no annotations), the description is inadequate. It lacks details on return values, error handling, or behavioral nuances like scanning limitations. Without annotations or output schema, the description should provide more context to be complete, but it falls short.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 67%, with parameters like 'table_name' well-documented but 'filter_expression' and 'expression_attributes' only partially described. The description adds no additional parameter semantics beyond the schema, such as examples or usage tips. Given the moderate coverage, the baseline of 3 is appropriate as the description doesn't compensate for gaps.

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 ('Scan') and resource ('items in a DynamoDB table'), making the purpose immediately understandable. It distinguishes from siblings like 'dynamodb_item_query' by specifying the scan operation, though it doesn't explicitly contrast them. The description is specific but lacks explicit sibling differentiation, warranting a 4.

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 'dynamodb_item_query' or 'dynamodb_item_get', nor does it mention prerequisites or context for scanning. It simply states what the tool does without indicating appropriate scenarios, resulting in minimal usage guidance.

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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