AI agents use update_resource to create or update resources in Amazon Data Processing MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Amazon Data Processing MCP Server environment.
The tool name 'update_resource' clearly indicates it modifies or changes existing resources. Without a description, the exact scope and reversibility cannot be fully determined, but 'update' typically implies reversible changes (Write category) rather than irreversible deletion (Destructive).
From the tool's definition Tool name is 'update_resource' which directly indicates modification of data or resources. Description is empty, limiting certainty.
Documented attack patterns abuse exactly the kind of access update_resource gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon Data Processing MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for update_resource:
{
"version": "1",
"default": "deny",
"tools": {
"update_resource": {
"limits": [
{
"counter": "update_resource_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
} update_resource 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.
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update_resource. It is categorised as a Write tool in the Amazon Data Processing MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Amazon Data Processing MCP Server MCP server in PolicyLayer and add a rule for update_resource: 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 Data Processing MCP Server. Nothing to install.
update_resource is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update_resource 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.
Set action: deny in the PolicyLayer policy for update_resource. 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.
update_resource is provided by the Amazon Data Processing MCP Server MCP server (awslabs.aws-dataprocessing-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Amazon Data Processing 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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805 Amazon Data Processing MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.