get_sagemaker_job_status

Read SageMaker pipeline execution status for a started notebook job.

Server AWS Notebook Runner MCP yummytastycode/aws-notebook-runner-mcp
Category Read
Risk class Low
Parameters 00 required

What get_sagemaker_job_status does on AWS Notebook Runner MCP

AI agents call get_sagemaker_job_status to retrieve information from AWS Notebook Runner MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

Why get_sagemaker_job_status needs a policy

This tool queries the state of an existing SageMaker job execution. It performs a pure read operation that retrieves data about a job's progress and status. No data is created, modified, deleted, or executed. The blast radius is minimal—worst case, an agent learns the status of a job it already started, which is informational only. The 'Read' category is appropriate.

From the tool's definition Tool name contains 'get_' and description states 'Read SageMaker pipeline execution status' — retrieves status information without modification or side effects.

Questions about get_sagemaker_job_status

What does the get_sagemaker_job_status tool do? +

Read SageMaker pipeline execution status for a started notebook job. It is categorised as a Read tool in the AWS Notebook Runner MCP MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on get_sagemaker_job_status? +

Register the AWS Notebook Runner MCP server in PolicyLayer and add a rule for get_sagemaker_job_status: 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 AWS Notebook Runner MCP. Nothing to install.

What risk level is get_sagemaker_job_status? +

get_sagemaker_job_status is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit get_sagemaker_job_status? +

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

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

get_sagemaker_job_status is provided by the AWS Notebook Runner MCP server (yummytastycode/aws-notebook-runner-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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