AI agents invoke container_stop to trigger actions in ChatGPT 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 executes a command that changes the operational state of a running container. While not destructive (the container persists and can be restarted), stopping a container is an active operation with real-world side effects on the running application—it interrupts service availability.
From the tool's definition Tool name: 'container_stop'. Description: 'Stop a running container'. This triggers an external operation (stopping a Docker container) whose effects depend on which container is targeted.
Documented attack patterns abuse exactly the kind of access container_stop gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and ChatGPT MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for container_stop:
{
"version": "1",
"default": "deny",
"tools": {
"container_stop": {
"limits": [
{
"counter": "container_stop_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}container_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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Stop a running container. It is categorised as a Execute tool in the ChatGPT MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the ChatGPT MCP Server MCP server in PolicyLayer and add a rule for container_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 ChatGPT MCP Server. Nothing to install.
container_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 container_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 container_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.
container_stop is provided by the ChatGPT MCP Server MCP server (toowiredd/chatgpt-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from ChatGPT 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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7 ChatGPT MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.