This record as markdown: /tools/io-github-portel-dev-ncp/deploy-server.md
What deploy_server does on Ncp
AI agents invoke deploy_server to trigger actions in Ncp. 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 deploy_server is rated High
Deploying a server instance is an Execute action—it triggers external infrastructure operations with effects determined by the arguments (server size, location, image, etc.).
From the tool's definition Tool name 'deploy_server' and description 'Deploy Vultr server instance' indicate executing infrastructure provisioning operations that create cloud resources.
Attacks that exploit this kind of access
The rule that runs deploy_server safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ncp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For deploy_server, this is the rule to start with:
deploy_server 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 Ncp, apply this rule, and every deploy_server call is checked against it from then on.
Questions about deploy_server
Deploy Vultr server instance. It is categorised as a Execute tool in the Ncp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Ncp MCP server in PolicyLayer and add a rule for deploy_server: 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 Ncp. Nothing to install.
deploy_server 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 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. 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 is provided by the Ncp MCP server (@portel/ncp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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