Cancel a running deployment
AI agents invoke cancel_deployment to trigger actions in Coolify MCP Tools. 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.
Cancelling a running deployment is an operational action that interrupts an active process. It is not purely destructive (no data is deleted), but it executes a control action against a live deployment pipeline. Misuse could halt critical application rollouts, leaving services in an inconsistent or partially deployed state, which gives it a high severity.
From the tool's definition Cancel a running deployment
Attacks that exploit this kind of access
Cancel a running deployment. It is categorised as a Execute tool in the Coolify MCP Tools MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Coolify MCP Tools 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 MCP Tools. 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 Tools MCP server (jplansink/coolify-mcp-tools). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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