deploy_spot_instance · Verda Cloud MCP Server MCP

WHAT IT DOES

What deploy_spot_instance does on Verda Cloud MCP Server

AI agents invoke deploy_spot_instance to trigger actions in Verda Cloud 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.

WHY IT NEEDS A POLICY

Why deploy_spot_instance needs a policy

Deploying cloud instances is an Execute action—it triggers external cloud provider operations whose effects (resource allocation, billing, network access) depend on arguments and cannot be undone without calling delete_instance. While it creates infrastructure, the actual deployment operation (not the persistence of the instance) is the primary action.

From the tool's definition Tool name is 'deploy_spot_instance' in a server described as 'management of Verda Cloud GPU instances' that 'allows users to deploy spot instances' and 'support[s] automated workflows'.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

deploy_spot_instance Rate-limited

deploy_spot_instance 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "deploy_spot_instance": {
      "limits": [
        {
          "counter": "deploy_spot_instance_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Verda Cloud MCP Server, so you can run agents against it safely.

FAQ

Questions about deploy_spot_instance

What does the deploy_spot_instance tool do? +

deploy_spot_instance. It is categorised as a Execute tool in the Verda Cloud MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on deploy_spot_instance? +

Register the Verda Cloud MCP Server MCP server in PolicyLayer and add a rule for deploy_spot_instance: 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 Verda Cloud MCP Server. Nothing to install.

What risk level is deploy_spot_instance? +

deploy_spot_instance is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit deploy_spot_instance? +

Yes. Add a rate_limit block to the deploy_spot_instance 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.

How do I block deploy_spot_instance completely? +

Set action: deny in the PolicyLayer policy for deploy_spot_instance. 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.

What MCP server provides deploy_spot_instance? +

deploy_spot_instance is provided by the Verda Cloud MCP Server MCP server (sniper35/verda-cloud-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.