AI agents invoke schedule_start_application to trigger actions in Amazon EKS 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 tool name implies triggering an application start operation on an EKS cluster, which is an Execute-category action (triggering external operations). Since the description is empty, confidence is reduced. On an EKS server, starting applications can have significant side effects depending on the workload, warranting high severity.
From the tool's definition Tool name 'schedule_start_application' suggests scheduling the start of an application on EKS, which triggers an external operation. Description is empty and uninformative, lowering confidence.
Documented attack patterns abuse exactly the kind of access schedule_start_application gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon EKS MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for schedule_start_application:
{
"version": "1",
"default": "deny",
"tools": {
"schedule_start_application": {
"limits": [
{
"counter": "schedule_start_application_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} schedule_start_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_start_application. It is categorised as a Execute tool in the Amazon EKS MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Amazon EKS MCP Server MCP server in PolicyLayer and add a rule for schedule_start_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 Amazon EKS MCP Server. Nothing to install.
schedule_start_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_start_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_start_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_start_application is provided by the Amazon EKS MCP Server MCP server (awslabs.eks-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Amazon EKS 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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