AI agents invoke stop_application to trigger actions in AWS Labs Aurora DSQL 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.
Stopping an application is an Execute action—it triggers external operational effects (service interruption, process termination) whose consequences depend on which application is targeted. While not irreversible like Destructive actions, it causes significant disruption and falls below Financial in severity. High severity due to potential service outages.
From the tool's definition Tool name 'stop_application' indicates termination of running application processes. Description is empty, limiting direct evidence, but the function name itself clearly indicates an action that halts external operations.
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 Labs Aurora DSQL 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 Labs Aurora DSQL MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the AWS Labs Aurora DSQL 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 Labs Aurora DSQL 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 Labs Aurora DSQL MCP Server MCP server (awslabs.aurora-dsql-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from AWS Labs Aurora DSQL 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 Labs Aurora DSQL MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.