cache_set · Amazon SageMaker AI MCP Server MCP

WHAT IT DOES

What cache_set does on Amazon SageMaker AI MCP Server

AI agents use cache_set 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 cache_set needs a policy

This tool modifies state by writing to a cache, making it a Write operation. The severity is medium because cache poisoning could affect AI agent decision-making or downstream operations, but the impact is typically contained to the cache layer and may be automatically invalidated. Confidence is high given the clear write semantics, though the impact depends on how the cache is used in the broader system.

From the tool's definition Tool name 'cache_set' and description 'Set a value in the cache' indicate the tool creates or modifies cached data. The operation is reversible—cache values can be overwritten or cleared—distinguishing it from destructive operations.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

cache_set Rate-limited

cache_set 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": {
    "cache_set": {
      "limits": [
        {
          "counter": "cache_set_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 cache_set

What does the cache_set tool do? +

Set a value in the cache. 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 cache_set? +

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

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

Can I rate-limit cache_set? +

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

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

cache_set 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.