Stop a running Hugo preview server
AI agents invoke stop_preview to trigger actions in Hugo. 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 executes a command to stop a running process, which falls under Execute category as it performs an action whose effects depend on the state of the system. However, severity is low because stopping a preview server is a benign, easily reversible operation with minimal blast radius—the preview can simply be restarted with no data loss or side effects. It does not modify, delete, or create permanent artifacts.
From the tool's definition Tool name 'stop_preview' and description 'Stop a running Hugo preview server' indicate execution of a command to terminate a process. The verb 'stop' represents an active operation that triggers external system behavior (terminating a server process).
Documented attack patterns abuse exactly the kind of access stop_preview gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Hugo, and nothing reaches the server without passing your rules. This is the rule we recommend for stop_preview:
{
"version": "1",
"default": "deny",
"tools": {
"stop_preview": {
"limits": [
{
"counter": "stop_preview_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} stop_preview 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 a running Hugo preview server. It is categorised as a Execute tool in the Hugo MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Hugo MCP server in PolicyLayer and add a rule for stop_preview: 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 Hugo. Nothing to install.
stop_preview 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_preview 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_preview. 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_preview is provided by the Hugo MCP server (sunnycloudyang/hugo-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Hugo, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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15 Hugo tools catalogued and risk-classified — across an index of 43,000+ MCP servers.