create_agent_runtime · Amazon SageMaker AI MCP Server MCP

WHAT IT DOES

What create_agent_runtime does on Amazon SageMaker AI MCP Server

AI agents use create_agent_runtime 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 create_agent_runtime needs a policy

The name 'create_agent_runtime' strongly implies creating a new agent runtime resource in SageMaker AI, which is a Write operation. However, because the description is empty, the exact behavior is uncertain. Creating an agent runtime in AWS SageMaker could have significant blast radius as it provisions cloud infrastructure/resources, hence high severity. Confidence is reduced due to lack of description.

From the tool's definition Tool name 'create_agent_runtime' suggests creation of a new resource; description is empty and uninformative.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

create_agent_runtime Rate-limited

create_agent_runtime 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": {
    "create_agent_runtime": {
      "limits": [
        {
          "counter": "create_agent_runtime_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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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 create_agent_runtime

What does the create_agent_runtime tool do? +

create_agent_runtime. 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 create_agent_runtime? +

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

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

Can I rate-limit create_agent_runtime? +

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

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

create_agent_runtime 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.