schedule_stop_application
AI agents invoke schedule_stop_application to trigger actions in CloudWatch Application Signals 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.
The name suggests this tool schedules the stopping of an application, which is an irreversible operational action (stopping a running service) that triggers external effects. It could cause downtime. The description is empty, which lowers confidence, but the name strongly implies an Execute-level action with high blast radius.
From the tool's definition Tool name 'schedule_stop_application' implies scheduling a stop action on an application, which is an external operational trigger.
Documented attack patterns abuse exactly the kind of access schedule_stop_application gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and CloudWatch Application Signals MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for schedule_stop_application:
{
"version": "1",
"default": "deny",
"tools": {
"schedule_stop_application": {
"limits": [
{
"counter": "schedule_stop_application_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} schedule_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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schedule_stop_application. It is categorised as a Execute tool in the CloudWatch Application Signals MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the CloudWatch Application Signals MCP Server MCP server in PolicyLayer and add a rule for schedule_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 CloudWatch Application Signals MCP Server. Nothing to install.
schedule_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 schedule_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 schedule_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.
schedule_stop_application is provided by the CloudWatch Application Signals MCP Server MCP server (awslabs.cloudwatch-applicationsignals-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from CloudWatch Application Signals 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 CloudWatch Application Signals MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.