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dynamodb_batch_execute

Execute multiple PartiQL statements in a batch to process DynamoDB operations efficiently.

Instructions

Execute multiple PartiQL statements in a batch

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statementsYesList of PartiQL statements to execute
parametersYesList of parameter lists for each statement

Implementation Reference

  • Core handler implementation for the dynamodb_batch_execute tool, invoking boto3 DynamoDB client's batch_execute_statement with the provided statements and parameters.
    elif name == "dynamodb_batch_execute":
        response = dynamodb_client.batch_execute_statement(
            Statements=[{
                'Statement': statement,
                'Parameters': params
            } for statement, params in zip(arguments["statements"], arguments["parameters"])]
        )
  • Schema definition for the dynamodb_batch_execute tool, specifying input requirements for statements and parameters.
    Tool(
        name="dynamodb_batch_execute",
        description="Execute multiple PartiQL statements in a batch",
        inputSchema={
            "type": "object",
            "properties": {
                "statements": {
                    "type": "array",
                    "description": "List of PartiQL statements to execute",
                    "items": {
                        "type": "string"
                    }
                },
                "parameters": {
                    "type": "array",
                    "description": "List of parameter lists for each statement",
                    "items": {
                        "type": "array"
                    }
                }
            },
            "required": ["statements", "parameters"]
        }
    ),
  • Tool registration via the MCP server's list_tools handler, which returns all AWS tools including dynamodb_batch_execute through get_aws_tools().
    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?

With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'execute' but does not clarify if this includes mutations, requires specific permissions, has atomicity guarantees, or handles errors. This leaves significant gaps for a tool that likely involves database operations.

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 with no wasted words. It is front-loaded and directly states the tool's function, 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 complexity of batch database operations, no annotations, and no output schema, the description is insufficient. It lacks details on behavior, error handling, return values, and usage context, making it incomplete for effective tool selection and invocation.

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?

The schema description coverage is 100%, so the schema already documents both parameters ('statements' and 'parameters') adequately. The description adds no additional meaning beyond what the schema provides, such as syntax examples or constraints, resulting in a baseline score.

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 ('execute') and resource ('multiple PartiQL statements in a batch'), making the purpose understandable. However, it does not explicitly differentiate from siblings like 'dynamodb_item_batch_write' or 'dynamodb_item_update', which could also involve batch operations, leaving some ambiguity.

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?

No guidance is provided on when to use this tool versus alternatives. For example, it does not specify if this is for read-only queries, write operations, or mixed batches, nor does it mention prerequisites or compare to sibling tools like 'dynamodb_item_batch_write'.

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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