AI agents invoke stop_application to trigger actions in AWS Support MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
A tool that stops an application performs an irreversible operational action that interrupts service. While not data deletion (Destructive), it executes a command with real-world side effects. The lack of description lowers confidence slightly, but the explicit 'stop' action in the name strongly suggests Execute classification. Severity is high because an AI agent misusing this could halt critical production systems.
From the tool's definition Tool named 'stop_application' with no description provided. The name indicates it triggers an external operation (stopping an application), which is characteristic of Execute category tools that run commands or trigger operations whose effects depend on…
Documented attack patterns abuse exactly the kind of access stop_application gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and AWS Support MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for stop_application:
{
"version": "1",
"default": "deny",
"tools": {
"stop_application": {
"limits": [
{
"counter": "stop_application_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} stop_application 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.
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stop_application. It is categorised as a Execute tool in the AWS Support MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the AWS Support MCP Server MCP server in PolicyLayer and add a rule for stop_application: 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 Support MCP Server. Nothing to install.
stop_application is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the stop_application 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 stop_application. 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.
stop_application is provided by the AWS Support MCP Server MCP server (awslabs.aws-support-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from AWS Support 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 AWS Support MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.