schedule_start_application
AI agents invoke schedule_start_application to trigger actions in AWS Documentation 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 suggests it schedules or triggers the start of an application, which is an external operation/execution action. However, the description is empty, so confidence is low. Based on the name alone, this appears to be an Execute-category action with high severity since starting applications can have significant side effects.
From the tool's definition Tool name: schedule_start_application — implies scheduling/triggering the start of an application
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 AWS Documentation 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 AWS Documentation MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the AWS Documentation 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 AWS Documentation 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 AWS Documentation MCP Server MCP server (awslabs.aws-documentation-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from AWS Documentation 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 Documentation MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.