manage_aws_glue_statements

THE RISK

High Risk

Why manage_aws_glue_statements needs a policy

AWS Glue 'statements' are code statements run within Glue interactive sessions (like Spark/Python code execution). 'Manage' implies lifecycle operations including running statements. The description is empty, lowering confidence, but the AWS Glue statements API is primarily about executing code in sessions.

From the tool's definition Tool name 'manage_aws_glue_statements' — 'manage' suggests create/run/delete operations; 'statements' in AWS Glue context typically refers to executing code statements in interactive sessions

Documented attack patterns abuse exactly the kind of access manage_aws_glue_statements gives an agent:

POLICY

How to control manage_aws_glue_statements

PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon SageMaker AI MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for manage_aws_glue_statements:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "manage_aws_glue_statements": {
      "limits": [
        {
          "counter": "manage_aws_glue_statements_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

manage_aws_glue_statements stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

Create a free account and register Amazon SageMaker AI MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

RATE-LIMIT THIS TOOL →

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EXPLORE

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High-risk tools in Amazon SageMaker AI MCP Server → Every high-risk tool this server exposes, in one view.
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FAQ

Questions about manage_aws_glue_statements

What does the manage_aws_glue_statements tool do? +

manage_aws_glue_statements. It is categorised as a Execute tool in the Amazon SageMaker AI MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on manage_aws_glue_statements? +

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

manage_aws_glue_statements is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit manage_aws_glue_statements? +

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

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

manage_aws_glue_statements 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.

Enforce policy on every Amazon SageMaker AI MCP Server tool call.

Start from Amazon SageMaker AI MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

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