deploy_server_docker
AI agents invoke deploy_server_docker to trigger actions in Megaraptor MCP. 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.
deploy_server_docker triggers real processes with real consequences. An agent gone sideways doesn't fire it once — it starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Attacks that exploit this kind of access
deploy_server_docker. It is categorised as a Execute tool in the Megaraptor MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Megaraptor MCP server in PolicyLayer and add a rule for deploy_server_docker: 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 Megaraptor MCP. Nothing to install.
deploy_server_docker 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 deploy_server_docker 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 deploy_server_docker. 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.
deploy_server_docker is provided by the Megaraptor MCP server (wagonbomb/megaraptor-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.