update_schedule_state · Amazon SageMaker AI MCP Server MCP

WHAT IT DOES

What update_schedule_state does on Amazon SageMaker AI MCP Server

AI agents use update_schedule_state 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 update_schedule_state needs a policy

The tool name suggests changing the state of a schedule (likely enabling/disabling or adjusting scheduling parameters in SageMaker). This is a Write operation as it modifies configuration reversibly. Without a description, confidence is moderate. Severity is medium because misuse could disrupt scheduled ML jobs or training operations, but changes are typically reversible.

From the tool's definition Tool name 'update_schedule_state' indicates modification of scheduling state/configuration. No description provided, but 'update' verbs consistently indicate reversible data modification rather than destruction or execution.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

update_schedule_state Rate-limited

update_schedule_state 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": {
    "update_schedule_state": {
      "limits": [
        {
          "counter": "update_schedule_state_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

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 update_schedule_state

What does the update_schedule_state tool do? +

update_schedule_state. 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 update_schedule_state? +

Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for update_schedule_state: 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 update_schedule_state? +

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

Can I rate-limit update_schedule_state? +

Yes. Add a rate_limit block to the update_schedule_state 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 update_schedule_state completely? +

Set action: deny in the PolicyLayer policy for update_schedule_state. 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 update_schedule_state? +

update_schedule_state 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.