Gracefully shut down the Caddy server. Requires confirm=true to prevent accidental shutdown.
AI agents invoke caddy_stop to trigger actions in Caddy. 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.
This tool triggers an external operation (server shutdown) that has significant operational consequences. While the shutdown is graceful (not forced), it is an Execute action because it invokes a command-line-like operation on a running system whose effects are immediate and not trivially reversible within normal operations.
From the tool's definition Gracefully shut down the Caddy server.
Documented attack patterns abuse exactly the kind of access caddy_stop gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Caddy, and nothing reaches the server without passing your rules. This is the rule we recommend for caddy_stop:
{
"version": "1",
"default": "deny",
"tools": {
"caddy_stop": {
"limits": [
{
"counter": "caddy_stop_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} caddy_stop 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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Gracefully shut down the Caddy server. Requires confirm=true to prevent accidental shutdown. It is categorised as a Execute tool in the Caddy MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Caddy MCP server in PolicyLayer and add a rule for caddy_stop: 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 Caddy. Nothing to install.
caddy_stop 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 caddy_stop 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 caddy_stop. 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.
caddy_stop is provided by the Caddy MCP server (yawlabs/caddy-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Caddy, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
18 Caddy tools catalogued and risk-classified — across an index of 43,000+ MCP servers.