This record as markdown: /tools/coolify/cancel-deployment.md
What cancel_deployment does on Coolify
AI agents invoke cancel_deployment to trigger actions in Coolify. 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.
Why cancel_deployment is rated High
Cancelling a deployment interrupts an active operation in progress. This is an external operation that triggers a state change in the deployment system. While it doesn't permanently delete data, it aborts a running process which can have significant side effects (e.g., partially deployed services, rollback requirements).
From the tool's definition Cancel a running deployment by UUID
Attacks that exploit this kind of access
The rule that runs cancel_deployment safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Coolify, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For cancel_deployment, this is the rule to start with:
cancel_deployment 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Coolify, apply this rule, and every cancel_deployment call is checked against it from then on.
Questions about cancel_deployment
Cancel a running deployment by UUID. It is categorised as a Execute tool in the Coolify MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Coolify MCP server in PolicyLayer and add a rule for cancel_deployment: 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 Coolify. Nothing to install.
cancel_deployment 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 cancel_deployment 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 cancel_deployment. 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.
cancel_deployment is provided by the Coolify MCP server (coolify-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
More on Coolify, and thousands of servers like it.
Across the catalogue