memory_batch_update_records

WHAT IT DOES

What memory_batch_update_records does on Amazon SageMaker AI MCP Server

AI agents use memory_batch_update_records to create or update resources in Amazon SageMaker AI MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Amazon SageMaker AI MCP Server environment.

WHY IT NEEDS A POLICY

Why memory_batch_update_records needs a policy

The name implies a Write operation — batch updating records. However, the description is empty, which lowers confidence. 'Update' is a reversible modification, placing this in the Write category. Severity is medium due to the batch nature potentially affecting many records at once. Without a description, we cannot confirm if it has destructive or more severe capabilities.

From the tool's definition Tool name 'memory_batch_update_records' suggests batch updating of records in memory/storage

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

memory_batch_update_records Rate-limited

memory_batch_update_records stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "memory_batch_update_records": {
      "limits": [
        {
          "counter": "memory_batch_update_records_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Amazon SageMaker AI MCP Server, so you can run agents against it safely.

FAQ

Questions about memory_batch_update_records

What does the memory_batch_update_records tool do? +

memory_batch_update_records. It is categorised as a Write tool in the Amazon SageMaker AI MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on memory_batch_update_records? +

Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for memory_batch_update_records: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Amazon SageMaker AI MCP Server. Nothing to install.

What risk level is memory_batch_update_records? +

memory_batch_update_records is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit memory_batch_update_records? +

Yes. Add a rate_limit block to the memory_batch_update_records rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block memory_batch_update_records completely? +

Set action: deny in the PolicyLayer policy for memory_batch_update_records. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides memory_batch_update_records? +

memory_batch_update_records is provided by the Amazon SageMaker AI MCP Server MCP server (awslabs.sagemaker-ai-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.