update_agent_runtime_endpoint

WHAT IT DOES

What update_agent_runtime_endpoint does on Amazon SageMaker AI MCP Server

AI agents use update_agent_runtime_endpoint 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_agent_runtime_endpoint needs a policy

The 'update_' prefix strongly suggests a write/modify operation, likely updating an agent runtime endpoint configuration in SageMaker. This could affect running inference endpoints. Description is empty, so confidence is reduced. Severity is high because modifying a runtime endpoint could disrupt production ML workloads.

From the tool's definition Tool name: update_agent_runtime_endpoint — description is empty/uninformative

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

update_agent_runtime_endpoint Rate-limited

update_agent_runtime_endpoint 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_agent_runtime_endpoint": {
      "limits": [
        {
          "counter": "update_agent_runtime_endpoint_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 update_agent_runtime_endpoint

What does the update_agent_runtime_endpoint tool do? +

update_agent_runtime_endpoint. 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_agent_runtime_endpoint? +

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

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

Can I rate-limit update_agent_runtime_endpoint? +

Yes. Add a rate_limit block to the update_agent_runtime_endpoint 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_agent_runtime_endpoint completely? +

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

update_agent_runtime_endpoint 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.